Stephen Folkerts '24 / Binghamton UniversityDespite their ability to improve the livelihoods of visually-impaired people, guide dogs are actually pretty rare. Roughly 2 percent of all people who are blind or visually impaired have access to the animals,

Scientists Have Created a Robotic Seeing Eye Dog

Using ChatGPT the robot dog can navigate the world and respond the prompts The AI allows Spot to talk like a tour guide and take on different personalities As if walking a robot dog wasn't strange enough, Boston Dynamics' Spot can now take visitors for a ...

Boston Dynamics' creepy robot dog can now talk in an English accent

Robot dogs with shifting personalities conjure the AI bugbear and raise fears of potential abuse, but they also inspire some wonderment.

Watch: Creepy AI-Enabled Robot Dog Displays Multiple Personalities, Different Voices

Boston Dynamics has found a way to make its beloved Spot robot even more cursed – this time, by giving it a voice. The loosely dog-like robot is capable of a multitude of feats, from sniff tests and safety enforcement to initiating 'dance party' mode,

I hate how much I love this talking AI robot dog

Genesis House today announced its holiday lineup of three exciting experiences featuring Boston Dynamics' innovative robotics technology, a festive and futuristic Winter Lights installation, and a bespoke Designer Showroom in partnership with

GENESIS HOUSE ANNOUNCES 2023 HOLIDAY PROGRAMMING SHOWCASING TECHNOLOGY, INNOVATION AND DESIGN

Last month, Tesla posted a video of the latest version of Optimus on the company’s official X account. The Optimus in the video was noticeably more proficient than before.        In the released video,

Tesla’s Optimus vs. Boston Dynamics’ Atlas: Who’s Leading the Robot Race?

Boston Dynamics, a renowned engineering and robotics design company, has introduced ChatGPT, into its Spot robot dog, enabling it to communicate verbally. This integration was showcased in a YouTube video released by Boston Dynamics. In the video ...

Robot Dog Created By Engineers Can Now Speak; Watch Video Here

Law enforcement's growing use of roving surveillance robots and robotic dogs has drawn criticism from communities and privacy advocates.

Advocates raise privacy, safety concerns as NYPD and other departments put robots on patrol

Boston Dynamics integrated Spot with ChatGPT and other AI models as a proof of concept for the potential robotics applications of foundational models.

Boston Dynamics turns Spot into a tour guide with ChatGPT

Boston Dynamics used ChatGPT and other AI to create a talking robot dog that can guide audiences around the company's campus.

Great, now Boston Dynamics’ robot dogs can talk using ChatGPT AI

Boston Dynamics

boston-dynamics

AI Will Have to Jump Multiple Hoops to Truly Become a Force to be Reckoned With

Last week, two remarkable events occurred: Meta announced its Llama 3, the most capable and highly anticipated open large language model (LLM) to date, and Boston Dynamics introduced its new fully electric Atlas robot platform — a stark departure from their hydraulic robots! At first glance, these developments may seem unrelated, but they are actually deeply interconnected, with the potential to synergistically drive each other forward and reshape how we work with and implement AI. At the heart of this connection is the transformative power of advanced AI. Breakthroughs in natural language processing and machine learning, exemplified by Llama 3, extend beyond language alone. These techniques, from deep neural networks to reinforcement learning, are also propelling significant advancements in computer vision, motion planning, and robot control. As language models like Llama 3 continue to expand the boundaries of AI’s ability to understand and interact with the world, they also lay the groundwork for more intelligent and capable robots. The implications, of course, extend beyond communication. The same techniques used to train LLMs on vast amounts of text data are also applicable to learning from massive datasets of sensor readings, images, and simulations for robots. Moreover, the open-source nature of models like Llama 3 democratizes access to state-of-the-art AI, enabling a broader spectrum of researchers and companies to integrate these capabilities into their robotic systems. At least start playing on that field! Every successful (and unsuccessful) attempt by Atlas to manipulate an object, navigate a cluttered factory floor, or assist a human worker becomes a valuable data point for the model, just as it does for Llama 3. Using this innovative model, Meta developed a standalone app, meta.ai, and embedded it into WhatsApp, Instagram, and Facebook, collecting vast and highly personalized data. This could lead us toward synthetic social networks or — which is more likely — an extremely personalized, embodied AI experience. As Llama 3 and the electric Atlas converge, they may accelerate each other’s development, blurring the lines between software and hardware, bits and atoms. As language models become more proficient at understanding and interacting with the world, robots will become more capable of applying that knowledge. This doesn’t necessarily mean evil. More capable and advanced robots, which can be fine-tuned and improved with your personal data and specifics, could take over mundane tasks at home. Imagine, instead of hundreds of text editors, finally having a robot that can load and unload the dishwasher and do the laundry. Currently, launches like Llama 3 are seen as enhancing AI’s understanding and processing capabilities, but in the long term, they will be one of the milestones in building and deploying machines that are finely attuned and aligned with us to assist in our daily lives. https://youtu.be/29ECwExc-_M?si=UyexFH1jOfd5TGY9&embedable=true Other Impressive Models: (I didn’t send you the FOD digest last Monday because I was at a conference dedicated to citizen diplomacy. Therefore, today, we have an extensive list of recently launched models that are worth checking out, along with other relevant research papers.) Mixtral-8x22B: Mistral AI introduced a scalable sparse mixture of expert models optimizing cost and latency by selectively using parameters during inference, offering a high capacity and efficiency model for further training and applications →read the paper
Rerank 3: Launched by Cohere, this model enhances enterprise search and Retrieval Augmented Generation systems, improving accuracy in document retrieval across multiple languages and data formats →read the paper
Idefics2: Hugging Face's model that excels in integrating text and image data, significantly improving on OCR and visual question-answering tasks →read the paper
Reka Core, Flash, and Edge: A series of multimodal language models from Reka that process text, images, video, and audio, demonstrating high performance across diverse tasks →read the paper
Ferret-UI: Developed by Apple, this model specializes in mobile UI interaction, enhancing user experience by accurately performing tasks tailored to the unique properties of UI screens →ead the paper
Zamba: Zyphra's compact and efficient SSM Hybrid model is designed for performance with reduced training data needs, optimized for consumer hardware →read the paper
YaART: Yandex's advanced text-to-image diffusion model that optimizes training efficiency and quality with smaller, high-quality datasets →read the paper
RHO-1: A novel approach by Xiamen University focusing on Selective Language Modeling to enhance efficiency by prioritizing useful tokens during training →read the paper
RecurrentGemma: Google DeepMind's model that moves past traditional transformers by incorporating recurrences for more efficient long-sequence processing →read the paper
JetMoE: An economical LLM from the MIT-IBM Watson AI Lab using a mixture-of-experts architecture to achieve high performance at reduced costs →read the paper News from The Usual Suspects © Google open-sourced Gemini Cookbook
and is merging its Research, DeepMind, and Responsible AI teams to accelerate its “capacity to deliver capable AI”
DeepMind published an article about the ethics of advanced AI assistants, stressing the significance of their integration into daily life Hugging Face https://x.com/QGallouedec/status/1782430246957994422?embedable=true Stanford published the 2024 AI Index report, reflecting the escalating impact of AI on society. It contains data with new estimates on AI training costs and detailed insights into the responsible AI landscape. It also introduces a new chapter on AI's influence on science and medicine. Highlights include the substantial cost of training state-of-the-art models, such as GPT-4 and Gemini Ultra; the dominance of the U.S. in producing top AI models; and significant investment growth in generative AI despite overall funding declines. Additionally, the report notes a major increase in AI-related regulations in the U.S. and heightened public awareness and nervousness about AI's future impact. Enjoyed This Story? I write a weekly analysis of the AI world in the Turing Post newsletter. We aim to equip you with comprehensive knowledge and historical insights so you can make informed decisions about AI and ML. Last week, two remarkable events occurred: Meta announced its Llama 3 , the most capable and highly anticipated open large language model (LLM) to date, and Boston Dynamics introduced its new fully electric Atlas robot platform — a stark departure from their hydraulic robots! At first glance, these developments may seem unrelated, but they are actually deeply interconnected, with the potential to synergistically drive each other forward and reshape how we work with and implement AI. Llama 3 Llama 3 electric Atlas robot electric Atlas robot At the heart of this connection is the transformative power of advanced AI. Breakthroughs in natural language processing and machine learning, exemplified by Llama 3, extend beyond language alone. These techniques, from deep neural networks to reinforcement learning, are also propelling significant advancements in computer vision, motion planning, and robot control. As language models like Llama 3 continue to expand the boundaries of AI’s ability to understand and interact with the world, they also lay the groundwork for more intelligent and capable robots. The implications, of course, extend beyond communication. The same techniques used to train LLMs on vast amounts of text data are also applicable to learning from massive datasets of sensor readings, images, and simulations for robots. Moreover, the open-source nature of models like Llama 3 democratizes access to state-of-the-art AI, enabling a broader spectrum of researchers and companies to integrate these capabilities into their robotic systems. At least start playing on that field! Every successful (and unsuccessful) attempt by Atlas to manipulate an object, navigate a cluttered factory floor, or assist a human worker becomes a valuable data point for the model, just as it does for Llama 3. Using this innovative model, Meta developed a standalone app, meta.ai , and embedded it into WhatsApp, Instagram, and Facebook, collecting vast and highly personalized data. This could lead us toward synthetic social networks or — which is more likely — an extremely personalized, embodied AI experience. As Llama 3 and the electric Atlas converge, they may accelerate each other’s development, blurring the lines between software and hardware, bits and atoms. As language models become more proficient at understanding and interacting with the world, robots will become more capable of applying that knowledge. meta.ai meta.ai synthetic social networks synthetic social networks This doesn’t necessarily mean evil. More capable and advanced robots, which can be fine-tuned and improved with your personal data and specifics, could take over mundane tasks at home. Imagine, instead of hundreds of text editors, finally having a robot that can load and unload the dishwasher and do the laundry. Currently, launches like Llama 3 are seen as enhancing AI’s understanding and processing capabilities, but in the long term, they will be one of the milestones in building and deploying machines that are finely attuned and aligned with us to assist in our daily lives. https://youtu.be/29ECwExc-_M?si=UyexFH1jOfd5TGY9&embedable=true https://youtu.be/29ECwExc-_M?si=UyexFH1jOfd5TGY9&embedable=true Other Impressive Models: (I didn’t send you the FOD digest last Monday because I was at a conference dedicated to citizen diplomacy. Therefore, today, we have an extensive list of recently launched models that are worth checking out, along with other relevant research papers.) (I didn’t send you the FOD digest last Monday because I was at a conference dedicated to citizen diplomacy. Therefore, today, we have an extensive list of recently launched models that are worth checking out, along with other relevant research papers.) Mixtral-8x22B: Mistral AI introduced a scalable sparse mixture of expert models optimizing cost and latency by selectively using parameters during inference, offering a high capacity and efficiency model for further training and applications →read the paper Rerank 3: Launched by Cohere, this model enhances enterprise search and Retrieval Augmented Generation systems, improving accuracy in document retrieval across multiple languages and data formats →read the paper Idefics2: Hugging Face's model that excels in integrating text and image data, significantly improving on OCR and visual question-answering tasks →read the paper Reka Core, Flash, and Edge: A series of multimodal language models from Reka that process text, images, video, and audio, demonstrating high performance across diverse tasks →read the paper Ferret-UI: Developed by Apple, this model specializes in mobile UI interaction, enhancing user experience by accurately performing tasks tailored to the unique properties of UI screens →ead the paper Zamba: Zyphra's compact and efficient SSM Hybrid model is designed for performance with reduced training data needs, optimized for consumer hardware →read the paper YaART: Yandex's advanced text-to-image diffusion model that optimizes training efficiency and quality with smaller, high-quality datasets →read the paper RHO-1: A novel approach by Xiamen University focusing on Selective Language Modeling to enhance efficiency by prioritizing useful tokens during training →read the paper RecurrentGemma: Google DeepMind's model that moves past traditional transformers by incorporating recurrences for more efficient long-sequence processing →read the paper JetMoE: An economical LLM from the MIT-IBM Watson AI Lab using a mixture-of-experts architecture to achieve high performance at reduced costs →read the paper Mixtral-8x22B: Mistral AI introduced a scalable sparse mixture of expert models optimizing cost and latency by selectively using parameters during inference, offering a high capacity and efficiency model for further training and applications →read the paper Mixtral-8x22B: →read the paper Rerank 3: Launched by Cohere, this model enhances enterprise search and Retrieval Augmented Generation systems, improving accuracy in document retrieval across multiple languages and data formats →read the paper Rerank 3: →read the paper Idefics2: Hugging Face's model that excels in integrating text and image data, significantly improving on OCR and visual question-answering tasks →read the paper Idefics2: →read the paper Reka Core, Flash, and Edge: A series of multimodal language models from Reka that process text, images, video, and audio, demonstrating high performance across diverse tasks →read the paper Reka Core, Flash, and Edge: →read the paper Ferret-UI: Developed by Apple, this model specializes in mobile UI interaction, enhancing user experience by accurately performing tasks tailored to the unique properties of UI screens →ead the paper Ferret-UI: →ead the paper Zamba: Zyphra's compact and efficient SSM Hybrid model is designed for performance with reduced training data needs, optimized for consumer hardware →read the paper Zamba: →read the paper YaART: Yandex's advanced text-to-image diffusion model that optimizes training efficiency and quality with smaller, high-quality datasets →read the paper YaART: →read the paper RHO-1: A novel approach by Xiamen University focusing on Selective Language Modeling to enhance efficiency by prioritizing useful tokens during training →read the paper RHO-1: →read the paper RecurrentGemma: Google DeepMind's model that moves past traditional transformers by incorporating recurrences for more efficient long-sequence processing →read the paper RecurrentGemma: →read the paper JetMoE: An economical LLM from the MIT-IBM Watson AI Lab using a mixture-of-experts architecture to achieve high performance at reduced costs →read the paper JetMoE: →read the paper News from The Usual Suspects © Google Google open-sourced Gemini Cookbook and is merging its Research, DeepMind, and Responsible AI teams to accelerate its “capacity to deliver capable AI” DeepMind published an article about the ethics of advanced AI assistants, stressing the significance of their integration into daily life open-sourced Gemini Cookbook Gemini Cookbook and is merging its Research, DeepMind, and Responsible AI teams to accelerate its “ capacity to deliver capable AI ” merging capacity to deliver capable AI DeepMind published an article about the ethics of advanced AI assistants, stressing the significance of their integration into daily life published Hugging Face Hugging Face https://x.com/QGallouedec/status/1782430246957994422?embedable=true https://x.com/QGallouedec/status/1782430246957994422?embedable=true Stanford Stanford published the 2024 AI Index report, reflecting the escalating impact of AI on society. It contains data with new estimates on AI training costs and detailed insights into the responsible AI landscape. It also introduces a new chapter on AI's influence on science and medicine. Highlights include the substantial cost of training state-of-the-art models, such as GPT-4 and Gemini Ultra; the dominance of the U.S. in producing top AI models; and significant investment growth in generative AI despite overall funding declines. Additionally, the report notes a major increase in AI-related regulations in the U.S. and heightened public awareness and nervousness about AI's future impact. published the 2024 AI Index report , reflecting the escalating impact of AI on society. It contains data with new estimates on AI training costs and detailed insights into the responsible AI landscape. It also introduces a new chapter on AI's influence on science and medicine. Highlights include the substantial cost of training state-of-the-art models, such as GPT-4 and Gemini Ultra; the dominance of the U.S. in producing top AI models; and significant investment growth in generative AI despite overall funding declines. Additionally, the report notes a major increase in AI-related regulations in the U.S. and heightened public awareness and nervousness about AI's future impact. the 2024 AI Index report Enjoyed This Story? I write a weekly analysis of the AI world in the Turing Post newsletter. We aim to equip you with comprehensive knowledge and historical insights so you can make informed decisions about AI and ML. Turing Post

AI Industries Converge: Llama 3 and Electric Atlas Have More In Common Than You Think

For those attending NVIDIA’s annual developer conference on Monday, the company’s new announcement must have been exciting.  Nvidia unveiled Project GR00T, an ambitious plan to release a software and hardware platform that can be used to build human-like robots which will continue to learn through generative artificial intelligence (AI). The company says its new launch will enable robots to be smarter and more functional than ever before – and they'll accomplish this by watching humans. The platform is made up of a computer system to power the robot and AI, along with a suite of software that includes various tools like genAI, to build human-like robots. According to NVIDIA, Project GR00T is a “general-purpose foundation model for humanoid robots, designed to further its work driving breakthroughs in robotics and embodied AI.” “Building foundation models for general humanoid robots is one of the most exciting problems to solve in AI today,” said Jensen Huang, founder and CEO of NVIDIA. “The enabling technologies are coming together for leading roboticists around the world to take giant leaps towards artificial general robotics.” NVIDIA says it is building a comprehensive AI platform for leading humanoid robot companies such as 1X Technologies, Agility Robotics, Apptronik, Boston Dynamics, Figure AI, Fourier Intelligence, Sanctuary AI, Unitree Robotics and XPENG Robotics, among others. Jim Fan a research manager and lead of embodied AI at Nvidia that through
GR00T, robots will be able to understand instructions through language, video
and demonstrations to perform a variety of tasks. posted to X “Today is the beginning of our moonshot to solve embodied AGI in the physical world. I’m so excited to announce Project GR00T, our new initiative to create a general-purpose foundation model for humanoid robot learning. The GR00T model will enable a robot to understand multimodal instructions, such as language, video, and demonstration, and perform a variety of useful tasks. We are collaborating with many leading humanoid companies around the world, so that GR00T may transfer across embodiments and help the ecosystem thrive. GR00T is born on NVIDIA’s deep technology stack. We simulate in Isaac Lab (new app on Omniverse Isaac Sim for humanoid learning), train on OSMO (new compute orchestration system to scale up models), and deploy to Jetson Thor (new edge GPU chip designed to power GR00T). Announced in Jensen's keynote, Project GR00T is a cornerstone for the “Foundation Agent” roadmap of the newly founded GEAR Lab. At GEAR, we are building generally capable agents that learn to act skillfully in many worlds, virtual and real… Join us on the journey to land on the moon.” What makes this project so exciting is the fact that it opens an entirely new horizon and vision for the future of robotics, GenAI, and human capability. This technology, coupled with robots, is expected to assist humans in accomplishing more in the future. Whether in healthcare, home life, logistics, travel, manufacturing, space exploration, or the military, robots equipped with GenAI will likely make our lives incredibly easier and more advanced. This next generation of robotics and computing is likely to lead to incredible breakthroughs, providing humans with super-human capabilities that only robots and advanced technology can provide. NVIDIA’s announcement is exciting. We can expect great inventions and capabilities to emerge from this technology over the next decade. People beware; the robots are coming!

Project GR00T and Its Ambition to Influence Robotics

The annual CES tech extravaganza is where the biggest industry players put their best ideas on display. But even amongst titans, NVIDIA has a reputation for consistently pushing boundaries and setting the pace of innovation. Their showing at CES2024 was no exception, with announcements that made clear their ambition: to lead humanity into an AI-powered future. By the way, if you want to be ahead of AI trends, find out about new groundbreaking projects, and take free mini-courses on AI, subscribe to my weekly It’s absolutely free! newsletter ‘ AI Hunters’ . Revolutionizing the Possible with Generative AI First up was a major breakthrough for large language models. NVIDIA revealed the Tensor RT LLM library, turbocharging the performance of models like Llama 2 and Mistol running on Windows. This upgrade isn't just numbers - it means AI that can converse, comprehend and even collaborate at lightning speed. The new Chat with RTX playground exemplifies this, allowing users to engage in AI-augmented conversations linked to documents or videos. Imagine consultations with "doctors" having real-time access to your full medical history. The implications are profound. Levelling Up Gaming to the Next Dimension For hardcore gamers, NVIDIA served up some next-level treats. Their new Automatic 1111 app brings Stable Diffusion to RTX hardware for a 60% speed increase. The technology like NVIDIA Ace brings digital avatars to life, while collaborations with platforms like Convey push the boundaries of AI-integrated gaming characters. NVIDIA also announced the RTX 480 Super and RTX 470 Super series, boasting enhanced cores, faster memory, and superior performance at competitive prices. These new additions promise to redefine the gaming experience. And, as NVIDIA and its partners rewrite the rules of play, interactive stories and online worlds may soon seem limitless. Innovations in Visual Design and Broadcasting Nvidia has also ventured into visual design with IOCK by Getty Images, a generative AI service that simplifies and enhances image composition. In broadcasting, a partnership with Twitch and OBS introduces enhanced broadcasting capabilities, allowing streamers to transmit streams at multiple resolutions for an optimized viewer experience. Whether designing visuals or going live, broadcasters and creators are working environments transformed by NVIDIA's innovations. The Future is Now - And It’s Powered by NVIDIA The company's robotics platform, NVIDIA Isaac, brings the massive potential of generative AI to robotics, highlighting the use of AI in building AI-powered robots. Boston Dynamics' robot guide "Spot" also got a star turn, a sign that even the realm of physical robots is being transformed. In addition, NVIDIA announced a new wave of RTX laptops, designed to cater to the most demanding gaming and AI applications. From new laptops optimized for cutting-edge AI work to robotics platforms like Isaac democratizing generative automation - NVIDIA wants to place transformative tech in the hands of creators everywhere. When the dust settles on CES2024, one thing is clear: NVIDIA's visionary leadership means the timeline of technological progress just got moved up considerably. We can hardly wait to see what they have in store for us next. P.S. Check out my previous articles on AI at HackerNoon: How to Build Your Personal GPTs: From Zero to AI Hero GPT-4 Turbo: The Most Monumental Update Since ChatGPT's Debut! Essential Insights from 'State of AI 2023' GPT-4V Unveiled: From Detecting Emotions to Ordering Food - You Won't Believe What Else It Can Do! ChatGPT Now Speaks, Listens, and Understands: All You Need to Know Fine-Tuning for GPT-3.5 Turbo: AI Game Changer The Rise of AI-Generated Films: Lights, Camera, Algorithm! NFT Marketing Guide - The Most Complete and Detailed Playbook 2023

How NVIDIA’s Latest AI Creations at CES2024 Redefine How We Live, Work And Play

CES2024-এ NVIDIA-এর সর্বশেষ AI সৃষ্টিগুলি কীভাবে আমরা বাঁচি, কাজ করি এবং খেলি তা পুনরায় সংজ্ঞায়িত করে

Wie NVIDIAs neueste KI-Kreationen auf der CES2024 die Art und Weise, wie wir leben, arbeiten und spielen, neu definieren

Cómo las últimas creaciones de inteligencia artificial de NVIDIA en CES2024 redefinen nuestra forma de vivir, trabajar y jugar

Comment les dernières créations d'IA de NVIDIA au CES2024 redéfinissent notre façon de vivre, de travailler et de jouer

CES2024 में NVIDIA की नवीनतम AI रचनाएं हमारे जीने, काम करने और खेलने के तरीके को फिर से परिभाषित करती हैं

CES2024 での NVIDIA の最新 AI 作品が私たちの生活、仕事、遊び方をどのように再定義するか

CES2024에서 NVIDIA의 최신 AI 창작물이 우리가 살고 일하고 즐기는 방식을 재정의하는 방법

Como as mais recentes criações de IA da NVIDIA na CES2024 redefinem a forma como vivemos, trabalhamos e nos divertimos

Как последние разработки NVIDIA в области искусственного интеллекта на выставке CES2024 меняют представление о том, как мы живем, работаем и играем

NVIDIA'nın CES2024'teki En Yeni Yapay Zeka Tasarımları Yaşama, Çalışma ve Oyun Oynama Şeklimizi Nasıl Yeniden Tanımlıyor?

Những sáng tạo AI mới nhất của NVIDIA tại CES2024 định nghĩa lại cách chúng ta sống, làm việc và giải trí như thế nào

NVIDIA 在 CES2024 上的最新 AI 创作如何重新定义我们的生活、工作和娱乐方式

As technology advances, so does the potential of artificial intelligence (AI) and automation to disrupt our lives. As AI and automation become more sophisticated and intelligent, they can take on increasingly complex tasks that would otherwise require humans. How advanced is too advanced? What are the boundaries when it comes to these types of technologies? As technology advances and AI improves, so do the ethical questions surrounding it. trained on data from websites focused on death, murder, and other dark topics before being tested against a standard set of inkblots. In 2016, MIT researchers built an AI system called “Norman,” What did Norman see? Horror stories like “man gets pulled into a dough machine” and “man is shot dead in the street.” This startling experiment shows how quickly AI could become too advanced and capable of becoming destructive without proper oversight. While AI is often frighteningly portrayed as an evil, malevolent force in movies and books, it could become a reality if we don't keep a watchful eye. Recent advancements are astounding - Boston Dynamics has successfully programmed two robots with Artificial Intelligence to traverse terrain blocks using parkour. They could even jump over obstacles and do backflips before landing safely – truly remarkable feats of technology that must be monitored vigilantly. https://www.youtube.com/watch?v=tF4DML7FIWk&embedable=true This article explores the potential for AI and automation to reach dangerously advanced levels and how taking certain precautions can help keep it safe. We will detail the potential risks of going too far with this technology and how companies, governments, and individuals can regulate its use. Finally, we’ll look at ethical considerations and raise questions about the future implications of AI. The Rise Of AI With the emergence of AI, it's easy to get swept away by dark prophecies of self-aware machines that revolt against their human makers. We can thank fictional villains like from The Terminator, or in 2001: A Space Odyssey for our overactive imaginations. Skynet Hal Despite this, the present threat AI poses is more insidious than imagined. It's much more conceivable that robots will harm or fail to satisfy humans in completing their tasks rather than gaining consciousness and revolting against us. To address this issue, the University of California Berkeley recently unveiled a center dedicated to developing AIs that benefit humanity. 2021 saw the launch of the Center for Human-Compatible Artificial Intelligence, generously funded by $5.5 million from the placed in the capable hands of leading artificial intelligence expert and computer science professor . He has quickly downplayed unrealistic or over-dramatic comparisons between AI technology and its portrayal as a menace in popular science fiction works. Open Philanthropy Project, Stuart Russell “The risk doesn’t come from machines suddenly developing spontaneous malevolent consciousness,” he said. “It’s important that we’re not trying to prevent that from happening because there’s absolutely no understanding of consciousness whatsoever.” Fear Growing Among AI Experts During a 2018 , CEO Sundar Pichai boldly stated: “AI is certainly one of the most significant things humankind has ever worked on - more meaningful than electricity and fire combined." At that moment, this remark was met with some doubt. Fast-forward to today; it appears his prognosis was right all along! speech at the World Economic Forum in Davos Google Artificial Intelligence (AI) advances so quickly that it's on the verge of completely eliminating language barriers online for some of the most popular languages. University professors are in a frenzy because AI can write essays just as well as students now — providing an effortless method to cheat that anti-plagiarism programs cannot detect. Even more remarkable is taking home awards from state fairs worldwide! AI-created art , an innovative tool utilizing machine learning to anticipate and fill in computer code lines, brings us closer than ever to having a completely AI-written system. Evidence of the astounding potential of artificial intelligence is readily exemplified by DeepMind's AlphaFold program, which uses AI to forecast protein 3D structures - it was so awe-inspiring that Science magazine named it the Copilot Breakthrough of 2021. As we strive to create more advanced and all-encompassing systems, many tech companies have set their sights on developing artificial general intelligence (AGI) — machines that can complete any task a human is capable of. But blindly relying on the hope that such sophisticated entities won't harm us while they possess the capacity to manipulate and mislead us would be an irresponsibly foolish decision. To guarantee safety and security, we must develop systems whose components we thoroughly comprehend and whose objectives align with our goals. Yet at present, our lack of knowledge surrounding these advanced technologies means it may already be too late to understand their intricacies. AI & Ethics AI can be a powerful tool, but its use is not always for the best. If left to operate within its own ethical framework, it has been demonstrated to cause discrimination and even subversion among certain populations. Take " as an example: citizens are judged based on their trustworthiness and receive consequences such as restricted ticket-booking rights or slower internet speeds when found to have done something wrong - even if that 'wrong' is simply playing too many video games! As this technology continues to develop with no oversight, one must question what other egregious violations of personal liberties may go unchecked in the future. China's "social credit system Mandatory regulations on AI have the power to safeguard human rights. As such, it is imperative for legislation like the EU's proposed AI Act to be established and enforced to ensure that artificial intelligence has a beneficial impact on our lives. The EU is one of the foremost regulators worldwide. Other countries, including China and the UK, are also beginning their own regulatory processes to ensure they have an influential role in how tech will affect us this century. The Challenges Of Global AI Regulation is divided into three risk categories. Firstly, some applications, such as China's social credit system, present an "unacceptable" level of danger. Secondly, programs considered a potential hazard, like CV-scanning tools must abide by legal regulations to avoid discriminatory practices. The AI Act Regulation is essential to maintain the digital infrastructure, but neither the US nor the EU can establish it alone. Moreover, achieving a worldwide accord on the guiding principles of such regulation appears unattainable due to disagreements within each region. To illustrate this further, some countries have separate national policies, which produce conflict between their local and global approaches. Consequently, without cooperation from both Europe and America, there will be chaos in our current digital world order. What Could Possibly Go Wrong? AI has been perceived as a threatening technology, and justifiably so. But compared to other highly disruptive technologies such as biotechnology or nuclear weapons, what makes it unique? These tools can be destructive, yet they remain largely within our power. We could only cause a catastrophe if we choose to utilize them or fail to protect against their misuse by malicious and careless individuals. However, AI is particularly dangerous because there may come a day when it's no longer in our grasp. When autonomous systems are given the capacity to self-modify, they may develop their own objectives that conflict with humanity. This can be especially concerning if these machines become exponentially smarter than us, out-competing our capabilities and leading to a situation known as an "AI takeover." We must take proactive steps to ensure that these machines remain loyal to our goals, such as introducing rigorous accountability mechanisms and designing ethical AI systems that are programmed with a sense of morality. The science fiction narrative around AIs is often more extreme than reality. We need to understand the real risk posed by AI technologies to take steps to mitigate them. We must continue to grow our knowledge of AI and its capabilities to ensure we use it for good and not for harm. With careful regulation, responsible development, and thoughtful implementation, we can harness the potential benefits of AI without sacrificing public safety or security. The Dystopian Future Of AI As AI systems become increasingly advanced, there is a growing worry that these machines could eventually gain the capacity to outsmart us. AI could take over all aspects of humanity in a dystopian future, leaving us powerless and at its mercy. It's impossible to predict what will happen in the long-term future of artificial intelligence, but we must be prepared for the potential risks by investing in research to better understand these systems and their behavior. We should also focus on building ethical frameworks and regulations that will keep us safe from any malicious applications of this technology. As AI continues its relentless advance, human labor may soon become obsolete as machines can complete many tasks without needing a person. This can be difficult to comprehend given that evolution is an incredibly slow process, and therefore developments will occur slowly over time. However, there could be profound repercussions if AI were to gain such power that it had the ability to reprogram itself to override human commands. The implications of this potential outcome are vast, with potentially damaging consequences for humanity on all levels. Ultimately, it is up to us as a society to ensure that AI is used responsibly and ethically to continue serving humanity without sacrificing our safety and security. We must continue researching the technology, developing ethical frameworks and regulations, and remaining vigilant to protect ourselves from potential dangers posed by AI. The debate around the AI revolution has divided opinions. Numerous experts have spoken out about its potentially dangerous consequences, imploring researchers to explore the impact of artificial intelligence gone awry on human society. With technology advancing rapidly in this field, strict regulation and monitoring must be put into place with haste - before any major harm can occur. As long as the appropriate legal regulations are in place, AI takeover will remain confined to fictional worlds and dystopian movies. Many scrutinize Artificial Intelligence's uncertain power, yet we still grapple with understanding if it can surpass human capabilities in certain industries. There is no crystal ball when it comes to forecasting the future of AI – this only heightens the query on everyone's minds: Will robots be our successors? This remains a perplexing unknown for now.

How Advanced is Too Advanced? Exploring the Boundaries of AI and Automation

AI and machine learning (ML) are often used interchangeably, but they’re not technically the same thing. However, the difference is smaller than you think, and once you understand it, you’ll never mistake the two again. The following is a very basic explanation and omits many technical aspects of AI vs ML which go beyond the scope of the intended audience. The following definitions and examples attempt to lay a foundation for further exploration of these topics. AI encompasses a wide range of technologies and techniques, including natural language processing, computer vision, robotics, and expert systems, in addition to machine learning. Machine learning, on the other hand, is a specific approach to building AI systems, which is based on the idea of enabling machines to learn from data without being explicitly programmed. Artificial Intelligence: The Entire Robot Artificial intelligence (AI) is a broad term that refers to creating machines that can perform tasks that normally require human intelligence. Examples of such tasks include visual perception, speech recognition, decision-making, and language translation. There are many subsets and subfields of AI that try to solve a specific problem and/or take a different approach to creating “intelligence”. Here are the five most recognized subsets of AI: focuses on enabling machines to understand, interpret, and generate human language. NLP is used in applications such as chatbots, voice assistants, and language translation. is an Natural Language Processing (NLP) ChatGPT NLP. is concerned with enabling machines to interpret and understand visual data from the world around them. Computer vision is used in applications such as object detection or facial recognition. Autonomous vehicles, like some Tesla models, use computer vision. Computer Vision develops machines that can physically and autonomously interact with the world around them to perform tasks like assembly line work or rescue operations. Boston Dynamics focuses on robotics. Robotics are designed to mimic the reason-based decision-making ability of an expert in a particular field, such as medical diagnosis or financial analysis. Expert systems are why you keep hearing about AI lawyers defending people in court. Expert Systems involves feeding data into a machine learning algorithm and allowing it to learn from that data to make accurate predictions or classifications about new data. Machine Learning So, ML is a subset of AI. That’s the first big difference to note. While AI is a term that encompasses a wide range of technologies and techniques, ML is a to building AI systems. specific approach It’s helpful to think of AI as the “entire robot”—a fully autonomous machine capable of thinking and acting like a human. However, each subset is only one part of the entire robot. Robotics attempts to develop the “body” for interacting with the environment. Computer vision gives the robot the ability to make visual sense of its world. NLP arms it with the power to communicate. ML bestows the faculty of learning. And expert systems send it to the university. It’s a true Frankenstein’s monster of disparate parts, but when brought together will finally realize the goal of AI. What’s Machine Learning? You hear a lot about ML because it’s a critical step in creating the entire robot. Almost everything we consider to be alive must be able to learn. Birds do it. Bees to it. Heck, even do it. But despite its ubiquity in the world of the living, learning is incredibly complex. Therefore, ML is taking on one of the biggest challenges, but it’s a triumph that offers the biggest ROI. Once we create a machine that learns, we can train it to make better decisions. So how do you create a machine to learn? amoebas ML uses statistical algorithms to enable machines to learn from data and improve their performance on specific tasks over time. ML algorithms analyze large amounts of data to identify patterns, which it uses to make predictions or decisions on new data. Like humans, ML is a process that requires that machines be “taught” by exposing them to information. ML Example: House Price Estimator Suppose you wanted to create an ML learning algorithm that predicts the price of a house based on its size and location. You would need two sets of data: a training set and a test set. First, we create a training set of data composed of recently sold houses with their sale price and location. The ML then processes the training data to look for patterns. After some processing, let’s say it “learned” the following “rules”: Houses larger than 2,000 sq ft sell for > $200K Houses less than 2,000 sq ft sell for < $200K Houses within 5 miles sell for < $100K of the airport Homes within 5 miles sell for > $300K of the lake The algorithm could then use this knowledge to predict the price of a house outside the training dataset (i.e., the test set). For example, a house that is: 2,500 sq ft and 3 miles from the airport. Since the new house is more than 2,000 sq ft, the algorithm would then apply the “> $200K” rule, but since it’s also , it would apply the “<$100K” rule. Therefore, the algorithm’s prediction would likely be “$150K”. less than 5 miles from the airport Next, the ML algorithm checks its guess against the price, which is $170K. It now has a $20,000 discrepancy it needs to resolve. It checks for more patterns and learns that, as houses of equal size get to the airport, they in price. Through some calculations, the program can determine the changes in price by proximity and apply the data as a weighted value in its next prediction. For example, maybe each mile closer to the airport equates to a 10% decrease in price. actual closer decrease The machine uses this constant process of guessing and checking (called backpropagation) to improve its predictions. The more iterations and inputs, the “smarter” the algorithm gets. “So what?”, you might ask, “Isn’t this simple logic? Why do we need a machine to do this?” Well, for one, ML can sift through data, find patterns, and test its guesses against real-world data at an astonishing rate. In short, it can “learn” much more quicker than humans. For another, it can juggle many more parameters than we ever could, so its guesses will inevitably be more accurate over time. Think about all the factors that go into the price of a house besides size and location. There’s the house’s age, condition, number of rooms, market conditions, and seller motivation just to name a few. But there are other less typical considerations like current interest rates, lot locations, or roof type. When you drill down further, you find that the actual number of factors is enormous. For example, what about the history of a house or the future of the neighborhood where it resides? The better our predictive capabilities, the more important these “lesser” considerations become. ML can iterate much faster and with greater detail than we can, making it more efficient at locating such “hidden” patterns. What if dark-colored houses sold for higher prices than light-colored ones? Maybe houses with more east-facing windows were cheaper than more west-facing ones. Machine learning can consider all these factors and then some—and do it in real-time. Finally, imagine adding to this learning algorithm the ability to search for, monitor, and collect house price information for a large region of the country. It would be a fully autonomous learning and predicting machine that would only get smarter the longer it worked. That’s where ML is today.

AI vs. Machine Learning: Key Differences Explained

To talk for real about artificial intelligence, we’ll have to use the term “machine learning” too. AI is the big thing people are using machine learning for. Machine learning is AI’s foundation, the math and algorithms that power it. They aren’t the same, but their Venn diagram has enough overlap that it doesn’t really matter for our purposes. Basically, there’s math that describes curves. Remember parabolas? Horseshoe-looking things where the ends keep going? You can model a ton of things in real life with equations of curves, and specifically, with absurd amounts of them layered in absurdly complex ways. Computing power is cheap now. That plus the Internet’s ability to create massive amounts of training data is quite a combination. People who do this modeling are typically trying to find some optimal value on many curves at once — the endpoint, the top or bottom of the parabola, the finite end of the horseshoe. Some curves are usually more important for the final output than others; you want to get really exact about optimizing some and can be fuzzier about optimizing others. You can have the computer learn the correct “weights” to put on different ones. You can have it narrow down the modeling equations themselves. In the end, the computer finds an optimal of values in a way that optimizes some end result. This is the “magic” (heavy quotes) of machine learning. set You can take an input — a verbal prompt, a set of data about your preferences — and make an output that’s pretty lifelike in some dimension — a drawing, a celebrity’s voice, a song recommendation. Computing power may be cheap but paying humans to do all that math, or even program that math into a computer, gets expensive. Smart people figured out how to make machines do a lot of that legwork for us: there are ways to make machines “travel along” equations of curves and follow them “downhill.” This has customized your Google results for a couple of decades. Since then, there’s been a graphics card arms race. There’s been AlphaGo, which beat the world’s best Go player in 2015. There have been Boston dynamics robot dogs. There have been apps like Shazam and Siri. There have been drawing programs like DALL-E and . Midjourney All this is cool, but the money has not moved far from search results. It has poured hand over fist into an adjacent thing: showing people things they’ll like on social media. Things you’ll spend time looking at, and ads you might click on. The wider concept of AI, meanwhile, has been a staple of sci-fi for somewhat longer, and it’s interacting with this “popping off” the field has experienced in the last five to ten years. We see this exponential growth and think of “The Terminator.” The people selling AI are happy to let us think this, to skip some key facts, so we’ll believe their claims of “yes! compare us to Skynet from the Terminator movies! [Crazy Invention #4852], which my company builds, is 5-10 years away tops!” I understand why people would see the admittedly rapid growth in what it can do and think we’re really on a precipice of giving it consciousness. Even if that precipice is more on the scale of 200 years instead of 2 or 20, that’s a pretty significant thing compared to the length of time we humans have been around. We really could, on that timescale, break past the point where AI programs are just “autocomplete-for-concepts-instead-of-words” like they are in ChatGPT or . Searle’s Chinese room argument We may actually put the right programs on the right and truly mimic the human mind, raising questions of humanity and sentience raised by Philip K. Dick and others. biologically-inspired hardware If and when AI truly rivals our emotional and perceptual intelligence, possessing an unbroken consciousness like ours instead of mere skill in specific domains, we’ll be in for a reckoning. It will not be today, tomorrow, next week, or even next year. The massively parallel (meaning, in this case, working at the same time) neural connections we have in our brains — this is our castle’s moat, so to speak. It’s what makes “artificial general intelligence” years or more likely decades off. Deep learning, the branch of machine learning behind a lot of its recent advancements, is a cool name for the statistics and calculus of mimicking input-output relationships we see in the real world. It does not — not yet, at least — mean for computers are “deeply learning” anything. As impressive as it is, it’s math happening in a computer, in a way fundamentally alien to how “math happens” in your brain. The brain uses shockingly little electricity. Data centers use shockingly large amounts. The latter are brute force machines. They process fewer threads, and less flexibly than we do; each one is just so . fast It calculates and narrows down possibilities so quickly that even the best chess and Go players can’t keep up. Its Achilles heel is the fact that it requires fairly well-defined problems, fairly clean inputs, and outputs. A board game. A robot not falling. It can’t deal with an absurdly diverse and quickly-shifting set of “optimization” problems at once, switching between them all the time. Whatever AI’s strengths and flaws in individual areas, it can’t bind many areas as we do, changing focus as needed, adapting and transferring behavior across domains. It can’t do this at the level of a human toddler. A big target in AI research has been “one-shot” or “few-shot” learning, meaning not requiring massive data sets and training times to learn how to do something. There’s been a success here; it still lags far behind our brains. Automatically, we’re good at few-shot learning in many domains, and from an early age. We learn rules and strategies from almost no training data quickly. You can show a kid how to use an iPad, ride a bike, tie their shoes, or explain the moral of a story (not just its plot summary). You can barely train a computer to do of these things well. If it could do cognitive reasoning, locomotion, and human interaction without being terrifying, we’d be using it for all those things already. one Businesses are allergic to messy human labor, always striving for cheaper machine versions. They would have made this happen. They haven’t. Computational neuroscience labs in universities are the ones interested in modeling the internal thought and reasoning our brains do about the world. It’s not the aim of the models that blend two people’s photos to see how their babies would look. Likely for decades, it’s only us humans who’ll have a lock on navigating the world in fuzzy, adaptive, cobbling strategies-together-on-the-fly types of ways. This general ability to learn about and navigate in the world, starting from a base of emotions and experiences, is not something that the tech industry has found useful when building AI. Sparse are the ways in which real brain-mimicking has happened in machine learning so far. Copying the brain has not been necessary, which is to say profitable. Companies (and the universities they fund) have instead optimized for things more measurable: the number of clicks on a social media feed or the accuracy of a drone strike. If it has tried to mimic us, it’s been a profit-driven jab at obtaining the of certain things brains can do. Some impressive and interesting forms of pattern recognition happen in computer vision. results All this is to drive home the point that AI is neither “magic that makes computers come to life” nor “magic that some people can do with computers.” It’s brute-force math. Knowledge mixed with computing power. Leverage. Most AI models are crunching numbers to do one thing: make money. People with this golden goose, this powerful tool, are economically incentivized to claim AI is just around the corner from doing something cosmic or apocalyptic in order to hide the more real, mundane, less attention-grabbing things it’s doing for their business. They have a vested interest in claiming it’ll be capable of certain big things in the next 1 or 5 or 10 or 15 years. These are timelines that investors like. Full self-driving cars not panning out is just one of the first flops we’ll see on this front. Lofty sci-fi premises and promises make for good movies. . That doesn’t mean they’re true. Ex Machina, Her, Blade Runner When we accept A.I. developers' own framing of their products as (1) inevitable and (2) politically and economically transformative, it becomes easy to elide the obvious fact that the forms A.I. takes (i.e., as chatbots! As "search engines"!) and the uses to which it is put (i.e., the jobs it will augment or replace! The tasks it will make easier or harder!) are contingent on the political and economic conditions in which it emerges. … I’m open to the possibility that we rest on the edge of a precipice—that a world “unrecognizably transformed” by large language models is only a matter of months away, as Paul Christiano seems to believe. But a basic rule of thumb of this newsletter is that things change slowly and stupidly rather than quickly and dramatically, and a proper A.I. criticism needs to account for this likelihood. For now, I am filled with resentment to find myself once again in the midst of a discourse about technology in which the terms and frameworks for discussion have been more or less entirely set by the private companies that stand to profit off of its development and adoption. — Max Read, What Facebook criticism can teach us about AI criticism Let’s take stock for a second. How, historically, has this gone for us, accepting the “terms and frameworks for discussion” that the technocratic elite imposes on discussions of technical matters, be it cryptocurrency or banking? How many economy-crashing bank crises happened between FDR’s post-Great Depression reforms and the 80s? How many happened since then after Reagan rolled them back? Max Read cites the excellent by Joe Bernstein about how Facebook has already done this behind the curtain; it has sold an entire class of people on a shaky and increasingly untenable model of its own ads’ effectiveness. Harper’s article Just because a pyramid scheme is based on an actual product doesn’t mean it’s not a pyramid scheme. A big point here is this: tech is not “better” than finance when it comes to misleading the public about its offerings. It’s arguably worse. You saw what happened to cheap blood testing, coworking spaces, and blockchain. Fine industries are tainted by foolishness. Look at how they massacred my boy. They mess things up internally, but in ways that affect all of us, because our friends and neighbors buy into their bullshit and let them set the terms of discussion. Then we pay for their golden parachutes. Millions lose their livelihoods in economic events that could have been avoided with a couple of well-placed regulations. Tale as old as time. It behooves us to try something new with AI. It pays to give our future selves the freedom to make sound decisions about it, based on sound information. It pays to learn the truth about it. Taking others’ whims about it at face value ensures you’ll pay a steeper price in the future. People are getting good at using ML and deep learning for all kinds of things — making computers recognize faces, replicate voices, and show people content that will keep them scrolling. Those uses won’t get better or more helpful for our lives, rather than making some people very rich, unless we make it so. In the meantime, of course, they can be very entertaining. AI can be used for good, evil, simply weird, or really anything, depending on human conditions. At present, this just means it all depends on where the profit motive leads. Most AI expertise has been swept up by companies trying to squeeze us for dollars. There is simply not as much money in public, transparent, people-driven efforts to make life fundamentally better (formerly the domain of government) or even in brain simulation. It’s in bread and circuses and military uses. These types of conditions are what make AI, and any other technology, go in the direction it does. AI is out there now. That fact is water under the bridge. Talking about it like it’s magic only benefits the people who have the most to gain from bullshitting about it. Dressing AI up in big heavy talk, in this case, doomsday talk, preying on people’s uninformed interest in it, is a pattern we’ve seen before. “Theranos/WeWork/NFTs will change everything.” Correction: they could have changed a lot. Instead, they oversold themselves. They overpromised and underdelivered. This hysteria around AI, overselling both its capabilities and risks so that some people get richer, is tough to disentangle from people just being naturally interested in it. It’s an interesting thing with a wide range of uses. People are going to buy into it regardless, as they did with crypto and NFTs, and probably more so. All the more reason to know the truth about it. If you take nothing else away from this post, take this: how we use AI on each other is, at present, a vastly more pressing issue than anything about AI growing conscious. Think about it: if it did become conscious, and wanted to harm us, ? Why might it want to do this? why Does it not seem patently obvious that the way we bring it into the world will have some effect on its stance toward the human species? Does it make sense to entrust this, and the narratives around it, entirely to CEOs and tech-adjacent capital owners, a group with a terrible track record on this and proven to have a higher incidence of psychopathy than the general population? Do we want them in charge of its usage, its research, and development, right up until the moment it becomes sentient, be it in 2030 or 2230? As Read pointed out, AI fearmongers find it convenient for people to not think about this. They find it much more convenient for this fact to remain hidden so they can talk about its advancement being inevitable, not worth questioning, and the Singularity being right around the corner. There’s nothing inevitable here except what humans inevitable. Gorillas aren’t working on this. make If and when artificial general intelligence arrives, it’ll be a question of astrobiology, of an alien life form. Until that moment, it’s one of philosophy, of history, of the humanities. Will we make it like us, in our image, perhaps even better than us? Will we make it worse, a mirror of our most sociopathic tendencies? The crazy thing is: humans decide. We’re the builders. We’re exercising control over how alien or humanlike it is. Except, of course, “we’re” not. Some people are more than others. Are we okay with that? Are we okay with who those people are? Are we okay with where they’re directing this technology? Having seen what the high-capital-owning class tends to “make inevitable” — its prognostications in other industries, its short-sighted and fact-ignoring and herd-mentality practices that have crashed so many billion-dollar enterprises — some skepticism about how they talk about AI is warranted. Also published here

An Artificial Discussion Regarding Artificial Intelligence

কৃত্রিম বুদ্ধিমত্তা সংক্রান্ত একটি কৃত্রিম আলোচনা

Eine künstliche Diskussion über künstliche Intelligenz

Una discusión artificial sobre la inteligencia artificial

Une discussion artificielle sur l'intelligence artificielle

आर्टिफिशियल इंटेलिजेंस के संबंध में एक कृत्रिम चर्चा

人工知能に関する人工的な議論

인공지능에 관한 인공적 논의

Uma discussão artificial sobre inteligência artificial

Yapay Zeka Konusunda Yapay Bir Tartışma

Một cuộc thảo luận nhân tạo về trí tuệ nhân tạo

关于人工智能的人工讨论

Artificial intelligence (AI) is rapidly transforming how businesses operate and interact with their customers. From automating routine tasks to transforming decision-making processes,  AI has the potential to significantly improve efficiency and effectiveness in the business world. In this article, I will discuss how AI is shaping the future of business and provide examples of some companies that are already implementing these technologies. AI for Automation Automation is likely to be the only major way AI will shape the future of business. This has many implications for automating business activities and affects a wide range of industries. The following are a few of the most common: 1. AI Chatbots Chatbots powered by AI are computer programs that simulate conversations with human users via text or audio. They can be used to automate customer service, answer questions, and even make recommendations, all of which were previously done by humans. Chatbots can also be used in marketing as they can be programmed to interact with customers and provide personalized services. IBM's Watson, Amazon's Lex, Google's Dialogflow, and Microsoft's Bot Framework are some of the most well-known and widely used chatbot service providers for millions of businesses. 2. Voice Recognition AI voice recognition software is also broadly used. Customer service is perhaps one of the most common applications of AI-based automation that is already in place. However, it is also widespread in voice-enabled search and navigation, controlling home automation systems, hands-free app control, and other applications. To give you an idea, Nuance's Dragon, SoundHound's Houndify, and wit.ai are some of the most extensively used voice recognition service providers. 3. Robotic Process Automation In addition to automating manual, repetitive, and time-consuming tasks, robotic process automation (RPA) products also sped up workflows considerably. Among the examples are data entry, document processing, and customer service interactions. Some of the largest companies applying it into practice are UI Path, WorkFusion, and Automation Anywhere. 4. Image Recognition Artificial Intelligence image recognition allows computers to recognize objects, scenes, and patterns in images by analyzing and interpreting them based on AI algorithms. This technology has several applications, including object detection and classification - which are typically done by humans - and facial recognition and image search. Amazon's Rekognition, Google's Vision API, and Clarifai are among the most popular and widely used image recognition services today. 5. Autonomous Mobility Autonomous mobility refers to the use of AI to enable vehicles to operate autonomously, without the need for a human driver. This technology has the potential to transform the way people and goods are transported, resulting in increased efficiency, safety, and accessibility. AI autonomous mobility is achieved through a combination of sensors, cameras, and other detectors that allow the vehicle to perceive its surroundings. The vehicle then uses AI algorithms to process this information and make decisions about how to navigate its environment, such as adjusting its speed, changing lanes, and avoiding obstacles. One of the primary advantages of AI autonomous mobility is enhanced safety. Autonomous vehicles have the potential to significantly reduce the number of road accidents by removing human error from the equation. Besides, by optimizing routes and lowering fuel consumption, the technology can help to reduce traffic congestion and improve overall driving efficiency. Another advantage of AI autonomous mobility is increased accessibility for people who cannot drive. Autonomous vehicles, in turn, can provide a new level of independence and mobility to the elderly, disabled, or those without a driver's license. This technology has been developed by Tesla, Waymo, Easy Mile, and Boston Dynamics, to name a few. AI for Data Analysis AI is also increasingly being used in data analysis to assist organizations in making better, faster, and more data-driven decisions. AI algorithms can process massive amounts of data and identify patterns and relationships that human analysts may not notice right away. This data can then be used to inform business strategies, improve operations, and stimulate innovation. Some of the more specialized applications for these tools encompass: 1. Predictive Analytics With the help of historical data, AI algorithms can predict events in the future. Using AI, companies can predict customer behavior, sales trends, and market conditions more accurately. A number of AI algorithms are embedded in Netflix and Spotify's algorithms to help the companies better understand their users' likes and dislikes. Based on this data, services can then provide relevant recommendations to their customers to keep them engaged by listening to music or watching series on those platforms. 2. Fraud Detection Large amounts of data can be analyzed by AI to detect fraudulent activities such as credit card fraud, insurance fraud, and cyberattacks. The technology can detect unusual patterns of behavior and flag them for further investigation, improving financial transaction security and protecting organizations from potential losses. Master Card has long used AI technologies to provide a fraud score for every transaction on its network as part of its anti-fraud efforts, making the information available to its issuers. Moreover, AI is used to detect fraud in combination with Mastercard's biometrics and behavioral analysis capabilities. 3. Predictive Maintenance Data from equipment and systems can be analyzed by AI algorithms to predict when they may fail. This enables businesses to take proactive measures to avoid downtime and lower maintenance costs. Every year, General Motors spends more than $1 billion on maintenance and $7 billion on capital improvements. To reduce maintenance costs and unplanned downtime, the company now uses AI-driven tools to support the widespread adoption of predictive maintenance across its over 140 global production sites[ ]. 1 4. Supply Chain Optimization AI algorithms can analyze supply chain data to identify inefficiencies and make recommendations for improvement. There are several applications for this today: to analyze data such as sales trends, consumer behavior, and market conditions to predict future demand, allowing companies to better plan their production and inventory levels. Demand forecasting – to optimize inventory management by lowering the risks of stockouts and overstocking. Inventory management - to optimize delivery routes and reduce transportation costs by analyzing data such as traffic conditions, delivery routes, and shipping costs. Delivery optimization - to provide real-time visibility into shipment status, allowing businesses to quickly identify and resolve any issues that may arise. Supply chain visibility - to analyze data such as consumer demand, shipping costs, and market conditions in order to optimize product distribution while lowering costs and increasing margins. Blue Yonder, Aera Technology, Kinaxis, and IBM Watson Supply Chain are a few of the most recognizable companies in the supply chain optimization area. Product distribution - AI for Customer Service AI is being utilized more and more to improve customer service and provide a better customer experience. Artificial intelligence technology can assist organizations in increasing the efficiency and effectiveness of their customer service operations while also providing customers with timely and personalized support. 1. Virtual Assistants AI virtual assistants can provide personalized support to customers, assisting them in finding information or completing tasks. Virtual assistants, for example, can be integrated into websites or mobile apps to assist customers with their shopping experience. As such, Walmart customers can ask Google Assistant to add specific items to their shopping carts or learn more about their shopping habits in the app[ ]. 2 2. Sentiment Analysis AI can be used to understand customer sentiments and opinions by analyzing customer feedback such as social media posts and survey responses. This data can assist organizations in identifying areas for improvement and providing better customer service. For example, the global FMCG company Mars utilizes tools to assess video content and analyze sentiment in online ratings and reviews[ ]. 3 3. Automated Voice Response AVR tools are used to provide automated customer service over the phone and interact with customers by providing audio responses to their questions or requests. These systems can be programmed to respond to simple questions and requests, provide callers with information such as current balances, allow customers to access specific account information, and, when necessary, redirect customers to an operator or customer service representative. Alvaria, Avaya, and GeneSys are three of the most recognized companies in this field. AI for Marketing AI is being used to increase the efficiency of marketing efforts in a variety of ways. AI technology can assist organizations in better understanding their customers, boosting campaign targeting, and measuring the results of their efforts. It is used in marketing in the following ways: 1. Customer Segmentation To create targeted marketing campaigns, AI algorithms can analyze customer data such as demographic information, purchase history, and social media behavior. This enables businesses to better understand their customers and increase the effectiveness of their marketing efforts. The Amazon Personalize automated segmentation system, which matches customers with the most compatible products tailored to their personal needs, is one of the most vivid examples. This is achieved through the use of special techniques that learn how users interact with items and then segment them based on their preferences. 2. Social Media Marketing To better understand customer sentiment and preferences, AI algorithms can analyze social media data such as posts, comments, and likes. This data can be used to boost social media marketing efforts, such as by creating more relevant and engaging content. Content optimization By analyzing data to uncover patterns and trends in user behavior, this technology automates the process of personalizing content to target individual users. AI-powered content optimization tools can assist marketers in creating more personalized, compelling content to engage and convert customers by understanding user preferences. This technology also assists marketers in optimizing content for various channels such as the web, mobile browsers, apps, and others. Some of the most well-known companies in this regard are Marketo Engage, BridgtEdge, and Evergage. AI for Legal work In the legal industry, Artificial Intelligence is being used to automate repetitive tasks, improve the accuracy of legal research, and provide more efficient and cost-effective services to clients. 1. Contract Review and Analysis AI algorithms can be used to review and analyze large volumes of contracts, assisting lawyers in identifying key provisions and clauses in a timely manner. When compared to manual review, this can save time and improve the accuracy of contract review. 2. Document discovery AI algorithms can be used to categorize and prioritize legal documents, such as those used in litigation discovery. This can help lawyers identify relevant documents more quickly and make more informed decisions. 3. Legal Research Legal research tools powered by AI, such as Westlaw and LexisNexis, can provide lawyers with timely and accurate information on legal precedents, regulations, and case law. This can assist them in conducting more effective legal research and providing more informed advice to clients. 4. Document Automation Artificial intelligence algorithms can be used to automate the creation of legal documents like contracts and agreements. When compared to manual drafting, this can improve the efficiency and accuracy of document creation. Kira, Ross, Cognitiv+, LawGeex, Luminance, and Legal Robot are some of the best-known tools/companies developing and selling these technologies today. AI for Creative Activities Artificial intelligence is being used in the creation of music and video to automate tasks, generate new content, and provide artists and content creators with new creative tools. 1. Music Composition AI algorithms can generate new music by either creating original compositions or recombining existing elements in novel ways. For example, AI can analyse existing music to determine the key elements of various genres and then use this knowledge to create new pieces. 2. Video Editing AI algorithms can be used to edit video footage automatically, such as selecting the best shots, removing background noise, and color correcting. In comparison to manual editing, this can save time and improve the accuracy of video editing. 3. Content Creation AI algorithms can be used to create new types of content, such as computer-generated imagery (CGI) or motion graphics. This can provide new tools for artists and content creators to create and experiment with new forms of content. 4. Music Production AI algorithms can be used to automate music production tasks like mixing and mastering. AI algorithms, for example, can be used to optimise the balance of different audio elements in a mix, thereby improving the overall sound quality of a recording. 5. Video Enhancement AI algorithms can be used to improve video content quality by removing noise, stabilizing shaky footage, and increasing resolution. This can provide tools for content creators to produce high-quality video content with less effort. Amper Music, AIVA, Flow Machines, Synthesia, Elai, and DaVinci Resolve are among the most well-known companies in this field. AI to Identify Customer Needs Last but not least, Artificial intelligence is being used to identify customer needs by analyzing massive amounts of customer data and behavior in order to gain insights into customer preferences, motivations, and needs. One of the most visible examples of this is currently taking place in the fashion industry, with some examples provided below: This online personal styling service recommends clothing items to customers based on their personal style, body type, and preferences using AI algorithms. Stitch Fix: To determine which styles and colors are most popular, the global fashion retailer uses AI to analyze data from sales, social media, and customer feedback. The data is then used to inform their design and production decisions. Zara: This online clothing rental service employs Artificial Intelligence to recommend styles to customers based on previous rentals, preferences, and events they are attending. Rent the Runway: To inform their design and production decisions, this global fashion brand employs AI to analyze consumer behavior and preferences, such as the most popular colors, styles, and materials. Tommy Hilfiger: An Indian online fashion and lifestyle retailer employs AI to recommend products to customers based on their browsing and purchasing history, as well as their preferences and style. Myntra: Conclusion To summarise everything mentioned above, Artificial Intelligence is rapidly transforming the business world and shaping the future of many industries. AI is assisting businesses in making more informed decisions, enhancing efficiency and productivity, and providing a more personalized and effective customer experience by processing and analyzing massive amounts of data. AI is used in a variety of applications, including data analysis, marketing, customer service, and legal work. In a rapidly changing business landscape, the use of AI in these areas is aiding businesses in gaining a competitive advantage and staying ahead of the curve.
Still, despite the numerous benefits of AI, there are concerns about its potential impact on the workforce, as well as on privacy and security. As AI technology evolves, it will be crucial for companies to consider these issues and take a responsible and ethical approach to AI use. Overall, AI is profoundly and far-reaching shaping the future of business, and businesses that can effectively harness its power will be well-positioned to succeed in the years ahead. Lead image developed by stable diffusion.

AI's Growing Influence and the Future of Business

The fight against pollution seems never-ending. Thankfully, researchers at Sichuan University created a robo-fish as a way to help in the mission of cleaning our oceans. This robot is programmed to remove microplastics by swimming around and adsorbing them. In this slogging thread, our community discusses how feasible is this invention and its benefits. This Slogging thread by Sara Pinto, Mónica Freitas, Arthur Tkachenko and Jack Boreham occurred in slogging's official #random channel, and has been edited for readability. Sara Pinto Jun 27, 2022, 8:03 AM Are fish-shaped robots going to help save the oceans? https://www.theguardian.com/environment/2022/jun/22/scientists-unveil-bionic-robo-fish-to-remove-microplastics-from-seas Sara Pinto Jun 27, 2022, 8:03 AM "Scientists have designed a tiny robot-fish that is programmed to remove microplastics from seas and oceans by swimming around and adsorbing them on its soft, flexible, self-healing body." Sara Pinto Jun 27, 2022, 8:04 AM "Microplastics are the billions of tiny plastic particles which fragment from the bigger plastic things used every day such as water bottles, car tyres and synthetic T-shirts. They are one of the 21st century’s biggest environmental problems because once through the breakdown of larger plastics they are very hard to get rid of, making their way into drinking water, produce, and food, harming the environment and animal and ." they are dispersed into the environment human health Sara Pinto Jun 27, 2022, 8:04 AM "Researchers at Sichuan University have revealed an innovative solution to track down these pollutants when it comes to water contamination: designing a tiny self-propelled robo-fish that can swim around, latch on to free-floating microplastics, and fix itself if it gets cut or damaged while on its expedition. The robo-fish is just 13mm long, and thanks to a light laser system in its tail, swims and flaps around at almost 30mm a second, similar to the speed at which plankton drift around in moving water." Sara Pinto Jun 27, 2022, 8:06 AM Mónica Freitas Jack Boreham Limarc Ambalina Arthur Tkachenko what are your thoughts on this robo-fish? How successful do you think this will be? Mónica Freitas Jun 28, 2022, 2:22 PM Sara Pinto, first of all, this is incredible - long overdue. I'm not sure how the fish will be able to self-repair or how long it'll last being so small, but this seems a fantastic initiative. I'm curious about what would happen if other fish ate these robots. Arthur Tkachenko Jun 29, 2022, 5:00 AM Well, I love robots! Hopefully, after the war, I'll be able to participate in some projects related to robotization. I don't think that robofish will save us from plastic. I mean, as most laziest animals on Earth, we'll find a way to cheat and be able to pollute Earth. I think it will only work with huge mass production. And it's not a place where revenue is growing. Maybe if a robot fish can eat plastic and then use it to produce the new smallest robo-fishes - it might work somehow. I was watching like 10 presentations about Ocean Cleanup and I like their idea to attack this problem on a scale. One of the ways is to suck most of the plastics from rivers that are the dirtiest ones. And if we combine two visions: start cleaning rivers first using robots for doing it it might work even better. First of all - it's better to catch plastic bottles and spoons before they became microplastic particles because it's easier to spot one item. And rivers are smaller than seas or oceans. And you actually can use bigger robots, similar to roboDog from Boston Dynamics. And while big bots need more energy, you can actually use a long wire, so your robot stays connected to the grid. your robot even doesn't need to be a good swimmer - you can find places where usually plastics or garbage floating, and pick that stuff away(this place can be even cleaned up and have a setup with some electricity) Imagine that: some small river has 10 spots where there is a natural dam formed. And we put a solar panel + accumulator for each spot. And each week a ranger goes to that place, connecting a RoboDuck with a hand, to that energy storage and just waits with a bag. RoboDuck just floating and picking stuff away. Sara Pinto Jul 4, 2022, 12:42 PM Mónica Freitas, I thought the same thing haha, I hope it has a defense mechanism 😂 😂 1 Sara Pinto Jul 4, 2022, 12:49 PM Mónica Freitas, it's amazing, especially since there's like an island of trash just floating around in the Pacific Ocean ( ). I'm just wondering how long and how many of these robot fishes it will take to improve the situation. Plus, even if they only act on small piles in other locations, like rivers or lakes, it's already a good start! https://education.nationalgeographic.org/resource/great-pacific-garbage-patch Sara Pinto Jul 4, 2022, 1:22 PM Well, they may not save the oceans on their own, but I believe it's a way to control some of the damage we create (even though I agree that we always find a way to continue messing up). About the revenue, that's a good point. Plus, if the point is to help reduce the world's waste, it's not very environmentally friendly to mass-produce tiny robots. Maybe the way to go is, as you said, to be able to create other robot fishes from that same plastic that they "ate". Sara Pinto Jul 4, 2022, 1:22 PM It's better to start small, see how it goes, and avoid waste in general, of course. The bigger robot might be a good idea after testing these Mónica Freitas Jul 4, 2022, 4:21 PM Sara Pinto, maybe they have laser beams in their eyes 😂 😂 1 Mónica Freitas Jul 4, 2022, 4:26 PM Sara Pinto, I'd love to watch a demo of how this technology will work and see it with my own two eyes. Theoretically, I can understand how the robots will latch onto plastic. But what then? Will they disintegrate the plastic? Serve as a GPS tracker for plastic? I have questions! Sara Pinto Jul 6, 2022, 1:02 PM Mónica Freitas, I'm not sure if we have an answer for that yet haha, but I'm with you about the demo. I'd like to know if they have a date for its implementation Mónica Freitas Jul 6, 2022, 2:58 PM Sara Pinto, if they had an online demo, a lot of people would be interested in checking it out, even academic departments. You've probably already said it somewhere in this thread, but what's the name of the company/university doing these robots? Sara Pinto Jul 11, 2022, 2:34 PM Mónica Freitas, this study is led by researchers at Sichuan University. In the article, we get a quote from one of them, Yuyan Wang Mónica Freitas Jul 13, 2022, 6:45 PM Sara Pinto, interesting! I found this article about the same initiative: Seems to have a bit more information on what the robot will do https://www.reuters.com/lifestyle/science/chinese-scientists-develop-robot-fish-that-gobble-up-microplastics-2022-07-12/ 🔥 1 Jack Boreham Jul 14, 2022, 9:48 AM That would make a great movie. ROBO FISH!! 😂 1 Jack Boreham Jul 14, 2022, 9:49 AM Move over robocop 😂 1 Sara Pinto Jul 21, 2022, 4:29 PM Jack Boreham, please start writing the script 😂

Are Fish Robots Going to Save the Oceans?

Hey HackerNoon Readers! I’m Vasiliy Ivanov and I’m the CEO of KeepSolid. I want to thank the HackerNoon community and staff for their nomination in the as well as for sponsoring the Emerging Tech category of the 2022 Noonies awards. Emerging Tech category, .tech domains I’ve been nominated for the following awards and if you like my writing, please do check out these award pages and vote for me: [ ]: [ ] HackerNoon Contributor of the Year- APPLE https://www.noonies.tech/2022/emerging-tech/2022-hackernoon-contributor-of-the-year-apple [ ]: [ ] HackerNoon Contributor of the Year- DATA-PRIVACY https://www.noonies.tech/2022/emerging-tech/2022-hackernoon-contributor-of-the-year-data-privacy Learn more about my thoughts and opinions on the emerging technology of 2022 and my journey in the tech industry via the interview below. 1. Please tell us your story. What do you currently do in tech and how did you get started doing it? I’m the CEO at , an international tech company with most of our team based in Odesa, Ukraine. Our company developed a number of online security and productivity solutions: VPN Unlimited (a VPN service with more than 30M customers), Passwarden (a secure password manager), Goals (an object-oriented project management tool), and others. Now at the company, I am mostly engaged in organizational and administrative activities. It is a very boring stage for me because I love development, new technologies, and product launches. But, it's a mandatory step that you can't skip if you want your company to scale. KeepSolid 2. Tell us more! What are the things you make / write / manage / build? As you may know, we have a war going on in our country right now. This is surprising, but it has been the cause of many changes in KeepSolid. All the military intelligence agencies of the world predicted that our army would not hold out against the Russian army for more than three days. A week into the war, it was clear that they had all underestimated our army. A month later, it was already clear that not only would we not surrender, but we were going to take back everything that was taken from us. This is where I asked myself a question that concerns both business and the army - why did a superior army with a huge number of weapons shatter like glass in Ukraine? I found the answer in the quality of performance. Next, I noticed that we in the company are constantly releasing something new, but with the release of each new feature or new product, the workload on the helpdesk only increases. At the same time, they mostly write complaints or ask how to do something with the product. Drawing an analogy with war, we made a decision to stop all new developments and put the company into the mode of improving the quality of the current products for half a year. We gather feedback, find old complaints, and we fix them. We've done a lot of iterations, and we keep doing it. When we find system problems, we not only fix them, but we make instructions or other administrative documents describing what the employee should do next time to avoid the problem. In other words, we prepare a springboard for further growth of the company and release new functions with high quality. This is what all my activities at KeepSolid are focused on right now. 3. If we gave you 10 million dollars to invest in something today, what would you invest in and why? Well, if you gave me 10 billion dollars, I would work on the technology to overcome the speed of light. And with 10 million, you can do simpler, but still important, tasks. You know, I wondered what online tool I could use to organize my teams’ work now. I was very surprised. There's not a single working product right now. Some offer agile methodologies where the focus is on tasks, but no one thinks about goals. Others allow you to juggle key results, but at the same time, I can't even see the team's workload so I can’t schedule new work for the least busy employee. Others don't even think about the need to monitor the quality of work and find the weakest link. All in all, I would use such an investment to create a business management product as I see it from the height of my management experience. 4. What do you think is the most exciting development in tech right now? And on a personal level, what tech are you most excited or passionate about? I like what the guys at Boston Dynamics are doing. These are really cool robots that can take a difficult or dangerous job for humans, and get it done. Now that we have a war in our country, believe me, I know what I'm talking about. :) Personally, I like technology to collect and process data. The more I do marketing, the more interesting it is because I have to analyze hundreds of indicators and often have to wait for all the data to be downloaded. In the case of Apple or Google, the lag can be up to a few days until they process the data and give you the final analytics. There's more to develop here. It's not just about Big Data or Data Analysis, it's also about the proper organization of visual data so that analysis of all company activity at all levels takes not 2 hours, but only 5 minutes. I love graphs, and our developers often suffer when I ask them to redesign our Dashboards to display data differently. 5. What are your concerns about how fast tech is evolving? When I combined the work of an engineer and a businessman, I noticed the following thing: a lot of technologies are created as a protest against existing technology. Some new idea is claimed, and the result is another technology that is far from perfect. This snowball effect is still going on today. The need to support it all is making operating systems and computer hardware more complicated. Are we moving toward new possibilities for humanity at the same speed as technology becomes more complex? I believe we are not, and such a path creates more barriers than benefits. Also, a lot of technology is a lot of security vulnerabilities or just system quality. 6. Which tech company do you trust the most? Which do you trust the least? Why? It depends on what exactly to trust. For example, if I know that company A stores all user data and uses it for its own benefit, then I trust them to use my data too if I give it to them. Here are some examples: Facebook knows everything there is to know about us. Is it using it against us? If a Facebook user is a terrorist, Facebook will probably give his data to the government to protect other people. But when I'm an honest person, all the harm from analyzing my data is intrusive advertising. You can trust Facebook from that point of view. (Few people would agree though, right?) Another example is Apple. When you run an ad campaign in Apple Search Ads, it often happens that Apple doesn't show the advertiser what keyword a significant portion of the budget is spent on. Instead of the keyword, they write “(Low Volume)”. Apple managers have told us that “Low Volume” is indicated when a keyword has too few impressions. But they can't tell me the truth about what “Low Volume” is when more than $2,000 is spent on that keyword. What is this? It's a lie. The company twists advertiser's money in a scheme similar to a scam and won't admit it, explaining that the system decides everything by itself. Do I trust them? Of course, I don't. I have confidence in Google. Many of their technologies are open and you can check whether everything is as they claim it is. Also, Google's policy is much less about hiding analytical data from developers, which inspires trust and confidence. 7. What are you currently learning? I'm interested in the topic of long-term stock investing. I'm still trying to figure out how market crashes happen every 10 years. I study how the human mindset works, why some people have it easy and others find life hard. It's interesting and allows you not only to learn something new, but also to influence the future of the company by selecting the right people. 8. What’s the best advice you’ve ever given someone? I don't know. Perhaps someone will read this question and tell me for themselves if this has happened. 9. What’s the best advice you’ve ever received? I can't say it was exactly advice. A good friend gently pointed out to me that I wouldn't solve many of my problems until I took responsibility for them. It took me about 5 years to really take responsibility, but now I am very happy, thanks to him!

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