Hollywood, Health & Society (HH&S), a program of the USC Annenberg Norman Lear Center, has unveiled the winners of the 2024 Sentinel Awards, which recognize some of the past year’s best TV entertainment that responsibly informs audiences on critical topics.

"Abbott Elementary," "Expats" and "Hacks" Are Among Shows Set To Receive Sentinel Awards - SHOOTonline

USC Annenberg Norman Lear Center's Hollywood, Health & Society program will honor advocacy stories on 'Grey's Anatomy' and more with Sentinel Awards.

‘Grey’s Anatomy,’ ‘Hacks,’ ‘The Simpsons’ Among Series to Be Honored by Hollywood, Health & Society’s Sentinel Awards

The 2024 Sentinel Awards, organized by the Hollywood, Health & Society program at the USC Annenberg Norman Lear Center, will honor shows like 'Hacks.'

Sentinel Awards 2024 Winners Include 'The Morning Show,' 'Hacks,' More

Laurens County will see nearly 500 new jobs come to the area after an international automotive parts company opens their new manufacturing facility.

More than 450 jobs are coming to Laurens County as auto parts company opens facility

Real-time index price for S&P 500 Growth (IGX), along with buy or sell indicators, analysis, charts, historical performance, news and more

Consumer Discretionary Stocks Are at It Again! Don’t Miss Out on These Potential Winners!

The United Auto Workers union says it reached a tentative contract with General Motors, the last of the Detroit Three automakers to agree to a deal. Under the deal reached

UAW reaches deal with General Motors that ends strikes against Detroit automakers pending votes

The supplier of seats for the Ford Bronco has given pay raises to workers at its plant in Detroit in a bid to keep them from leaving for other jobs and to protect its business as it ramps up for the post-strike production rush.

UAW strike spurs pay raise for workers at Ford supplier

Fintel reports that on October 30, 2023, Barclays maintained coverage of Lear (NYSE:LEA) with a Overweight recommendation. Analyst Price Forecast Suggests 36.07% Upside As of October 5, 2023, the average one-year price target for Lear is 174.

Barclays Maintains Lear (LEA) Overweight Recommendation

Fintel reports that on October 30, 2023, Benchmark reiterated coverage of Lear (NYSE:LEA) with a Buy recommendation. Analyst Price Forecast Suggests 36.07% Upside As of October 5, 2023, the average one-year price target for Lear is 174.

Benchmark Reiterates Lear (LEA) Buy Recommendation

Automotive tier-1 supplier Lear Corporation has reported its results for the third quarter of 2023, with a sales increase of 10% year-on-year to US$5.8 billion.

Fifth consecutive quarter of growth for Lear Corp in Q3

Lear

lear

Table of Links Abstract and 1 Introduction 2 Background & Problem Statement 2.1 How can we use MLLMs for Diffusion Synthesis that Synergizes both sides? 3 DreamLLM 3.1 End-to-End Interleaved generative Pretraining (I-GPT) 3.2 Model Training 4 Experiments and 4.1 Multimodal Comprehension 4.2 Text-Conditional Image Synthesis 4.3 Multimodal Joint Creation & Comprehension 5 Discussions 5.1 Synergy between creation & Comprehension? 5. 2 What is learned by DreamLLM? 6 Related Works 7 Conclusions and References A Additional Experiments B Additional Qualitative Examples C Implementation Details D Additional Related Works E Limitations, Failure Cases & Future Works 2 BACKGROUND & PROBLEM STATEMENT This paper is available on arxiv under CC BY-NC-ND 4.0 DEED license. Authors:
(1) Runpei Dong, Xi’an Jiaotong University and Internship at MEGVII;
(2) Chunrui Han, MEGVII Technology;
(3) Yuang Peng, Tsinghua University and Internship at MEGVII;
(4) Zekun Qi, Xi’an Jiaotong University and Internship at MEGVII;
(5) Zheng Ge, MEGVII Technology;
(6) Jinrong Yang, HUST and Internship at MEGVII;
(7) Liang Zhao, MEGVII Technology;
(8) Jianjian Sun, MEGVII Technology;
(9) Hongyu Zhou, MEGVII Technology;
(10) Haoran Wei, MEGVII Technology;
(11) Xiangwen Kong, MEGVII Technology;
(12) Xiangyu Zhang, MEGVII Technology and a Project leader;
(13) Kaisheng Ma, Tsinghua University and a Corresponding author;
(14) Li Yi, Tsinghua University, a Corresponding authors and Project leader. Table of Links Abstract and 1 Introduction Abstract and 1 Introduction 2 Background & Problem Statement 2 Background & Problem Statement 2.1 How can we use MLLMs for Diffusion Synthesis that Synergizes both sides? 2.1 How can we use MLLMs for Diffusion Synthesis that Synergizes both sides? 3 DreamLLM 3 DreamLLM 3.1 End-to-End Interleaved generative Pretraining (I-GPT) 3.1 End-to-End Interleaved generative Pretraining (I-GPT) 3.2 Model Training 3.2 Model Training 4 Experiments and 4.1 Multimodal Comprehension 4 Experiments and 4.1 Multimodal Comprehension 4.2 Text-Conditional Image Synthesis 4.2 Text-Conditional Image Synthesis 4.3 Multimodal Joint Creation & Comprehension 4.3 Multimodal Joint Creation & Comprehension 5 Discussions 5.1 Synergy between creation & Comprehension? 5.1 Synergy between creation & Comprehension? 5. 2 What is learned by DreamLLM? 5. 2 What is learned by DreamLLM? 6 Related Works 6 Related Works 7 Conclusions and References 7 Conclusions and References A Additional Experiments A Additional Experiments B Additional Qualitative Examples B Additional Qualitative Examples C Implementation Details C Implementation Details D Additional Related Works D Additional Related Works E Limitations, Failure Cases & Future Works E Limitations, Failure Cases & Future Works 2 BACKGROUND & PROBLEM STATEMENT This paper is available on arxiv under CC BY-NC-ND 4.0 DEED license. This paper is available on arxiv under CC BY-NC-ND 4.0 DEED license. available on arxiv Authors: (1) Runpei Dong, Xi’an Jiaotong University and Internship at MEGVII; (2) Chunrui Han, MEGVII Technology; (3) Yuang Peng, Tsinghua University and Internship at MEGVII; (4) Zekun Qi, Xi’an Jiaotong University and Internship at MEGVII; (5) Zheng Ge, MEGVII Technology; (6) Jinrong Yang, HUST and Internship at MEGVII; (7) Liang Zhao, MEGVII Technology; (8) Jianjian Sun, MEGVII Technology; (9) Hongyu Zhou, MEGVII Technology; (10) Haoran Wei, MEGVII Technology; (11) Xiangwen Kong, MEGVII Technology; (12) Xiangyu Zhang, MEGVII Technology and a Project leader; (13) Kaisheng Ma, Tsinghua University and a Corresponding author; (14) Li Yi, Tsinghua University, a Corresponding authors and Project leader. Authors: Authors: (1) Runpei Dong, Xi’an Jiaotong University and Internship at MEGVII; (2) Chunrui Han, MEGVII Technology; (3) Yuang Peng, Tsinghua University and Internship at MEGVII; (4) Zekun Qi, Xi’an Jiaotong University and Internship at MEGVII; (5) Zheng Ge, MEGVII Technology; (6) Jinrong Yang, HUST and Internship at MEGVII; (7) Liang Zhao, MEGVII Technology; (8) Jianjian Sun, MEGVII Technology; (9) Hongyu Zhou, MEGVII Technology; (10) Haoran Wei, MEGVII Technology; (11) Xiangwen Kong, MEGVII Technology; (12) Xiangyu Zhang, MEGVII Technology and a Project leader; (13) Kaisheng Ma, Tsinghua University and a Corresponding author; (14) Li Yi, Tsinghua University, a Corresponding authors and Project leader.

Get to Know More About DreamLLM: The Background on This Learning Framework

Table of Links Abstract and 1 Introduction 2 Background & Problem Statement 2.1 How can we use MLLMs for Diffusion Synthesis that Synergizes both sides? 3 DreamLLM 3.1 End-to-End Interleaved generative Pretraining (I-GPT) 3.2 Model Training 4 Experiments and 4.1 Multimodal Comprehension 4.2 Text-Conditional Image Synthesis 4.3 Multimodal Joint Creation & Comprehension 5 Discussions 5.1 Synergy between creation & Comprehension? 5. 2 What is learned by DreamLLM? 6 Related Works 7 Conclusions and References A Additional Experiments B Additional Qualitative Examples C Implementation Details D Additional Related Works E Limitations, Failure Cases & Future Works ABSTRACT This paper presents DREAMLLM, a learning framework that first achieves versatile Multimodal Large Language Models (MLLMs) empowered with frequently overlooked synergy between multimodal comprehension and creation. DREAMLLM operates on two fundamental principles. The first focuses on the generative modeling of both language and image posteriors by direct sampling in the raw multimodal space. This approach circumvents the limitations and information loss inherent to external feature extractors like CLIP, and a more thorough multimodal understanding is obtained. Second, DREAMLLM fosters the generation of raw, interleaved documents, modeling both text and image contents, along with unstructured layouts. This allows DREAMLLM to learn all conditional, marginal, and joint multimodal distributions effectively. As a result, DREAMLLM is the first MLLM capable of generating free-form interleaved content. Comprehensive experiments highlight DREAMLLM’s superior performance as a zero-shot multimodal generalist, reaping from the enhanced learning synergy. 1 INTRODUCTION “What I cannot create, I do not understand.” Richard P. Feynman, on his blackboard at the time of his death, 1988 Content comprehension and creation in multimodality are crucial and among the ultimate courses of machine intelligence (Sternberg, 1985; Legg & Hutter, 2007). To this end, Multimodal Large Language Models (MLLMs) (Alayrac et al., 2022; Hao et al., 2022; Huang et al., 2023) have emerged as extensions of the successful GPT-style Large Language Models (LLMs) (Brown et al., 2020; Zhang et al., 2022; OpenAI, 2022; 2023; Chen et al., 2023b; Touvron et al., 2023a;b) into visual realm. Recognized as foundation models (Bommasani et al., 2021), MLLMs have achieved unprecedented progress in multimodal comprehension capabilities. These advanced models typically enhance LLMs by incorporating images as multimodal inputs, such as CLIP features (Radford et al., 2021), to facilitate language-output multimodal comprehension. Their aim is to capture multimodal conditional or marginal distributions via a language posterior. However, multimodal creation, which involves generating images, texts, or both, necessitates a universal generative model that simultaneously learns language and image posteriors—currently underexplored. Until very recently, some concurrent works have shown success in conditional image generation using MLLMs (Koh et al., 2023; Sun et al., 2023b). As depicted in Fig. 1, these methods compel MLLMs to produce either discrete or continuous conditional embeddings that explicitly align with a pretrained CLIP encoder, which could later be used by a pretrained Stable Diffusion (SD) (Rombach et al., 2022) model for image generation. However, due to an inherent modality gap (Liang et al., 2022), CLIP semantics focus predominantly on modality-shared information, often overlooking modality-specific knowledge that could enhance multimodal comprehension. Consequently, these studies have not fully realized the potential learning synergy between multimodal creation and comprehension, have shown only marginal improvements in creativity, and remain deficient in multimodal comprehension. In this work, we introduce DREAMLLM, universally learning image and text posteriors with expected creation & comprehension synergy, based on the following two de-facto designing principles: i. Generate Everything as It Is Different from existing works that generate intermediate image representations like CLIP embeddings during training, DREAMLLM not only takes all modalities raw data as inputs but also as outputs in a truly end-to-end fashion (i.e., outputs are identical to inputs, see Fig. 1). The challenge lies in enabling MLLMs to learn the image posterior without compromising their comprehension capabilities. To address this, we introduce dream queries, a set of learnable embeddings that encapsulate the semantics encoded by MLLMs. This approach avoids altering the output space of MLLMs. Raw images are then decoded by the SD image decoder conditioned on these semantics. In this fashion, the pretrained SD acts as the score function (Ho et al., 2020). The image posterior is thus modeled by direct sampling in the pixel space, facilitated by score distillation (van den Oord et al., 2018; Poole et al., 2023). ii. Interleaved Generative Pre-Training (I-GPT) DREAMLLM is trained to generate interleaved multimodal corpora from the internet (Zhu et al., 2023b), both encoding and decoding interleaved image-text multimodal inputs. Unlike encoding multimodal inputs as in existing methods, decoding interleaved multimodal outputs is challenging due to the complex interleaving layout structures and the long-context requirement of image. Our approach tackles the interleaved layout learning using a unique token that predicts the placement of images within texts. Harnessing DREAMLLM’s causal nature, all contents are generated with history multimodal contexts of any length. This interleaved generative pretraining (I-GPT) inherently forms all joint, marginal, and conditional distributions of images and texts in the document, leading to a learning synergy that grounds DREAMLLM’s comprehension in creation and vice versa. Extensive experiments across various vision-language comprehension, content creation, and languageonly tasks demonstrate DREAMLLM’s superior performance as a zero-shot multimodal generalist. For instance, DREAMLLM-7B achieves an 8.46 FID on MS-COCO and sets a new standard with 49.1/35.9 scores on MMBench and MM-Vet evaluations, respectively. Moreover, we delve into the learning synergy between comprehension and creation, revealing decent in-context generation capabilities. With I-GPT pretraining, DREAMLLM generates interleaved documents following human prompts after supervised fine-tuning on instruction-following data, curated with GPT-4. To our knowledge, this work is the first to enable MLLMs to create free-form interleaved content with a learning synergy on both sides. As a foundational learning framework, DREAMLLM is adaptable across all modalities, laying a promising foundation for future multimodal learning research. This paper is available on arxiv under CC BY-NC-ND 4.0 DEED license. Authors:
(1) Runpei Dong, Xi’an Jiaotong University and Internship at MEGVII;
(2) Chunrui Han, MEGVII Technology;
(3) Yuang Peng, Tsinghua University and Internship at MEGVII;
(4) Zekun Qi, Xi’an Jiaotong University and Internship at MEGVII;
(5) Zheng Ge, MEGVII Technology;
(6) Jinrong Yang, HUST and Internship at MEGVII;
(7) Liang Zhao, MEGVII Technology;
(8) Jianjian Sun, MEGVII Technology;
(9) Hongyu Zhou, MEGVII Technology;
(10) Haoran Wei, MEGVII Technology;
(11) Xiangwen Kong, MEGVII Technology;
(12) Xiangyu Zhang, MEGVII Technology and a Project leader;
(13) Kaisheng Ma, Tsinghua University and a Corresponding author;
(14) Li Yi, Tsinghua University, a Corresponding authors and Project leader. Table of Links Abstract and 1 Introduction Abstract and 1 Introduction 2 Background & Problem Statement 2 Background & Problem Statement 2.1 How can we use MLLMs for Diffusion Synthesis that Synergizes both sides? 2.1 How can we use MLLMs for Diffusion Synthesis that Synergizes both sides? 3 DreamLLM 3 DreamLLM 3.1 End-to-End Interleaved generative Pretraining (I-GPT) 3.1 End-to-End Interleaved generative Pretraining (I-GPT) 3.2 Model Training 3.2 Model Training 4 Experiments and 4.1 Multimodal Comprehension 4 Experiments and 4.1 Multimodal Comprehension 4.2 Text-Conditional Image Synthesis 4.2 Text-Conditional Image Synthesis 4.3 Multimodal Joint Creation & Comprehension 4.3 Multimodal Joint Creation & Comprehension 5 Discussions 5.1 Synergy between creation & Comprehension? 5.1 Synergy between creation & Comprehension? 5. 2 What is learned by DreamLLM? 5. 2 What is learned by DreamLLM? 6 Related Works 6 Related Works 7 Conclusions and References 7 Conclusions and References A Additional Experiments A Additional Experiments B Additional Qualitative Examples B Additional Qualitative Examples C Implementation Details C Implementation Details D Additional Related Works D Additional Related Works E Limitations, Failure Cases & Future Works E Limitations, Failure Cases & Future Works ABSTRACT This paper presents DREAMLLM, a learning framework that first achieves versatile Multimodal Large Language Models (MLLMs) empowered with frequently overlooked synergy between multimodal comprehension and creation. DREAMLLM operates on two fundamental principles. The first focuses on the generative modeling of both language and image posteriors by direct sampling in the raw multimodal space. This approach circumvents the limitations and information loss inherent to external feature extractors like CLIP, and a more thorough multimodal understanding is obtained. Second, DREAMLLM fosters the generation of raw, interleaved documents, modeling both text and image contents, along with unstructured layouts. This allows DREAMLLM to learn all conditional, marginal, and joint multimodal distributions effectively. As a result, DREAMLLM is the first MLLM capable of generating free-form interleaved content. Comprehensive experiments highlight DREAMLLM’s superior performance as a zero-shot multimodal generalist, reaping from the enhanced learning synergy. 1 INTRODUCTION “What I cannot create, I do not understand.” Richard P. Feynman, on his blackboard at the time of his death, 1988 Richard P. Feynman, on his blackboard at the time of his death, 1988 Content comprehension and creation in multimodality are crucial and among the ultimate courses of machine intelligence (Sternberg, 1985; Legg & Hutter, 2007). To this end, Multimodal Large Language Models (MLLMs) (Alayrac et al., 2022; Hao et al., 2022; Huang et al., 2023) have emerged as extensions of the successful GPT-style Large Language Models (LLMs) (Brown et al., 2020; Zhang et al., 2022; OpenAI, 2022; 2023; Chen et al., 2023b; Touvron et al., 2023a;b) into visual realm. Recognized as foundation models (Bommasani et al., 2021), MLLMs have achieved unprecedented progress in multimodal comprehension capabilities. These advanced models typically enhance LLMs by incorporating images as multimodal inputs, such as CLIP features (Radford et al., 2021), to facilitate language-output multimodal comprehension. Their aim is to capture multimodal conditional or marginal distributions via a language posterior. However, multimodal creation, which involves generating images, texts, or both, necessitates a universal generative model that simultaneously learns language and image posteriors—currently underexplored. Until very recently, some concurrent works have shown success in conditional image generation using MLLMs (Koh et al., 2023; Sun et al., 2023b). As depicted in Fig. 1, these methods compel MLLMs to produce either discrete or continuous conditional embeddings that explicitly align with a pretrained CLIP encoder, which could later be used by a pretrained Stable Diffusion (SD) (Rombach et al., 2022) model for image generation. However, due to an inherent modality gap (Liang et al., 2022), CLIP semantics focus predominantly on modality-shared information, often overlooking modality-specific knowledge that could enhance multimodal comprehension. Consequently, these studies have not fully realized the potential learning synergy between multimodal creation and comprehension, have shown only marginal improvements in creativity, and remain deficient in multimodal comprehension. In this work, we introduce DREAMLLM, universally learning image and text posteriors with expected creation & comprehension synergy, based on the following two de-facto designing principles: i. Generate Everything as It Is Different from existing works that generate intermediate image representations like CLIP embeddings during training, DREAMLLM not only takes all modalities raw data as inputs but also as outputs in a truly end-to-end fashion (i.e., outputs are identical to inputs, see Fig. 1). i. Generate Everything as It Is The challenge lies in enabling MLLMs to learn the image posterior without compromising their comprehension capabilities. To address this, we introduce dream queries, a set of learnable embeddings that encapsulate the semantics encoded by MLLMs. This approach avoids altering the output space of MLLMs. Raw images are then decoded by the SD image decoder conditioned on these semantics. In this fashion, the pretrained SD acts as the score function (Ho et al., 2020). The image posterior is thus modeled by direct sampling in the pixel space, facilitated by score distillation (van den Oord et al., 2018; Poole et al., 2023). ii. Interleaved Generative Pre-Training (I-GPT) DREAMLLM is trained to generate interleaved multimodal corpora from the internet (Zhu et al., 2023b), both encoding and decoding interleaved image-text multimodal inputs. Unlike encoding multimodal inputs as in existing methods, decoding interleaved multimodal outputs is challenging due to the complex interleaving layout structures and the long-context requirement of image. ii. Interleaved Generative Pre-Training (I-GPT) Our approach tackles the interleaved layout learning using a unique token that predicts the placement of images within texts. Harnessing DREAMLLM’s causal nature, all contents are generated with history multimodal contexts of any length. This interleaved generative pretraining (I-GPT) inherently forms all joint, marginal, and conditional distributions of images and texts in the document, leading to a learning synergy that grounds DREAMLLM’s comprehension in creation and vice versa. Extensive experiments across various vision-language comprehension, content creation, and languageonly tasks demonstrate DREAMLLM’s superior performance as a zero-shot multimodal generalist. For instance, DREAMLLM-7B achieves an 8.46 FID on MS-COCO and sets a new standard with 49.1/35.9 scores on MMBench and MM-Vet evaluations, respectively. Moreover, we delve into the learning synergy between comprehension and creation, revealing decent in-context generation capabilities. With I-GPT pretraining, DREAMLLM generates interleaved documents following human prompts after supervised fine-tuning on instruction-following data, curated with GPT-4. To our knowledge, this work is the first to enable MLLMs to create free-form interleaved content with a learning synergy on both sides. As a foundational learning framework, DREAMLLM is adaptable across all modalities, laying a promising foundation for future multimodal learning research. This paper is available on arxiv under CC BY-NC-ND 4.0 DEED license. This paper is available on arxiv under CC BY-NC-ND 4.0 DEED license. available on arxiv Authors: (1) Runpei Dong, Xi’an Jiaotong University and Internship at MEGVII; (2) Chunrui Han, MEGVII Technology; (3) Yuang Peng, Tsinghua University and Internship at MEGVII; (4) Zekun Qi, Xi’an Jiaotong University and Internship at MEGVII; (5) Zheng Ge, MEGVII Technology; (6) Jinrong Yang, HUST and Internship at MEGVII; (7) Liang Zhao, MEGVII Technology; (8) Jianjian Sun, MEGVII Technology; (9) Hongyu Zhou, MEGVII Technology; (10) Haoran Wei, MEGVII Technology; (11) Xiangwen Kong, MEGVII Technology; (12) Xiangyu Zhang, MEGVII Technology and a Project leader; (13) Kaisheng Ma, Tsinghua University and a Corresponding author; (14) Li Yi, Tsinghua University, a Corresponding authors and Project leader. Authors: Authors: (1) Runpei Dong, Xi’an Jiaotong University and Internship at MEGVII; (2) Chunrui Han, MEGVII Technology; (3) Yuang Peng, Tsinghua University and Internship at MEGVII; (4) Zekun Qi, Xi’an Jiaotong University and Internship at MEGVII; (5) Zheng Ge, MEGVII Technology; (6) Jinrong Yang, HUST and Internship at MEGVII; (7) Liang Zhao, MEGVII Technology; (8) Jianjian Sun, MEGVII Technology; (9) Hongyu Zhou, MEGVII Technology; (10) Haoran Wei, MEGVII Technology; (11) Xiangwen Kong, MEGVII Technology; (12) Xiangyu Zhang, MEGVII Technology and a Project leader; (13) Kaisheng Ma, Tsinghua University and a Corresponding author; (14) Li Yi, Tsinghua University, a Corresponding authors and Project leader.

What Is DreamLLM? Everything You Need to Know About the Learning Framework

HackerNoon: What is your company in 2–5 words? Lewis Johnson: AI-powered human learning technology Why is now the time for your company to exist? We at Alelo became experts in applying conversational AI to education and training long before AI became fashionable. Now that the world has woken up to the potential of generative AI, people increasingly come to us for AI-powered learning solutions. What do you love about your team, and why are you the ones to solve this problem? I love our team’s shared passion and commitment for what we do, which is to use conversational AI to create quality learning products that make a difference in people’s lives. We understand the power of conversation to promote learning, which gives us a unique perspective. A key focus of ours is ensuring that our AI-powered solutions are trustworthy. I see too many AI-powered products out there that make mistakes and are unreliable. If you weren’t building your startup, what would you be doing? My other passion is singing, and if I were weren’t building my startup I would be making more music instead. At the moment, how do you measure success? What are your metrics? We measure success by the number of successful learners and happy customers. We provide solutions to organizations with large numbers of learners, so that we can have as much impact as possible. 500,000 learners to date, with potential for rapid increase. In a few sentences, what do you offer to whom? Our primary industry focus right now is healthcare. We work with health systems to create Virtual Patient Navigators that answer patient questions and point them to educational resources, thereby reducing burdens on care teams. We also offer training solutions to training organizations and health systems, that let health professionals practice their skills with simulated patients and caregivers. We also get demand from pharmaceutical companies and other providers of educational content, to create Navigators that support their customers**.** What’s most exciting about your traction to date? What’s exciting is to me is that we are acquiring customers with really large numbers of learners. One of our customers has a site that has reached 100 million downloads. That translates into really large numbers of learners, generating lots of recurring revenue for us. Where do you think your growth will be next year? I think our growth next year will be 100% YoY, and the year after that 1,000% YoY. Tell us about your first paying customer and revenue expectations over the next year. Our first paying customer was the US Government. We reached a lot of learners by working with the Government, but it was mainly through fixed contracts instead of recurring revenue. The new generative AI platform that we are developing has much more recurring revenue potential.  We project $2M next year, with an exponential growth curve kicking in toward the end of next year. What’s your biggest threat? Our biggest threats are our biggest suppliers: the large language model developers such as Google and OpenAI. They provide foundational technology for us for now, but they might try to offer competing solutions in the future. So far, their offerings in healthcare are untrustworthy and even unsafe, which gives us an advantage. This startup founder interview template is based on HackerNoon Founder & CEO David Smooke’s ten questions for startup founders. Would you like to take a stab at answering some of these questions? The link for the template is HERE. HackerNoon : What is your company in 2–5 words? HackerNoon Lewis Johnson : AI-powered human learning technology Lewis Johnson Lewis Johnson Why is now the time for your company to exist? We at Alelo became experts in applying conversational AI to education and training long before AI became fashionable. Now that the world has woken up to the potential of generative AI, people increasingly come to us for AI-powered learning solutions. Alelo What do you love about your team, and why are you the ones to solve this problem? I love our team’s shared passion and commitment for what we do, which is to use conversational AI to create quality learning products that make a difference in people’s lives. We understand the power of conversation to promote learning, which gives us a unique perspective. A key focus of ours is ensuring that our AI-powered solutions are trustworthy. I see too many AI-powered products out there that make mistakes and are unreliable. If you weren’t building your startup, what would you be doing? My other passion is singing, and if I were weren’t building my startup I would be making more music instead. At the moment, how do you measure success? What are your metrics? We measure success by the number of successful learners and happy customers. We provide solutions to organizations with large numbers of learners, so that we can have as much impact as possible. 500,000 learners to date, with potential for rapid increase. In a few sentences, what do you offer to whom? Our primary industry focus right now is healthcare. We work with health systems to create Virtual Patient Navigators that answer patient questions and point them to educational resources, thereby reducing burdens on care teams. We also offer training solutions to training organizations and health systems, that let health professionals practice their skills with simulated patients and caregivers. We also get demand from pharmaceutical companies and other providers of educational content, to create Navigators that support their customers**.** What’s most exciting about your traction to date? What’s exciting is to me is that we are acquiring customers with really large numbers of learners. One of our customers has a site that has reached 100 million downloads. That translates into really large numbers of learners, generating lots of recurring revenue for us. Where do you think your growth will be next year? I think our growth next year will be 100% YoY, and the year after that 1,000% YoY. Tell us about your first paying customer and revenue expectations over the next year. Our first paying customer was the US Government. We reached a lot of learners by working with the Government, but it was mainly through fixed contracts instead of recurring revenue. The new generative AI platform that we are developing has much more recurring revenue potential.  We project $2M next year, with an exponential growth curve kicking in toward the end of next year. What’s your biggest threat? Our biggest threats are our biggest suppliers: the large language model developers such as Google and OpenAI. They provide foundational technology for us for now, but they might try to offer competing solutions in the future. So far, their offerings in healthcare are untrustworthy and even unsafe, which gives us an advantage. This startup founder interview template is based on HackerNoon Founder & CEO David Smooke’s ten questions for startup founders. This startup founder interview template is based on HackerNoon Founder & CEO David Smooke ’s ten questions for startup founders . HackerNoon Founder & CEO David Smooke ten questions for startup founders Would you like to take a stab at answering some of these questions? The link for the template is HERE. Would you like to take a stab at answering some of these questions? The link for the template is HERE. The link for the template is HERE.

Revolutionizing Learning with AI: An Interview with Alelo's President & CEO

The journey of self-discovery often leads us to confront uncomfortable truths about human relationships and personal worth. As we navigate the complexities of connection and identity, we must learn to balance caring for others with maintaining our own sense of self. Through my experiences, I've observed a common pattern that transcends gender and culture: the tendency to lose ourselves in the pursuit of approval and acceptance. This pattern often manifests in relationships where one person gradually diminishes their own dreams, aspirations, and well-being in an attempt to prove their worth through perpetual sacrifice. However, true connection—whether in friendship, romance, or professional relationships—flourishes not through self-abandonment but through mutual growth and respect. The most enduring bonds are forged between individuals who maintain their distinct identities while supporting each other's evolution. Consider the paradox of personal value: the more we compromise our core principles and abandon our path in an attempt to please others, the more we undermine the very qualities that make us worthy partners in any relationship. Our unique perspectives, ambitions, and boundaries aren't obstacles to meaningful connections—they're essential ingredients. I've learned that self-respect isn't selfishness; it's the foundation upon which all healthy relationships are built. When we maintain our personal standards, pursue our goals, and honor our boundaries, we demonstrate not just self-love but also respect for others. We show that we value authenticity over approval, substance over superficial acceptance. The path to genuine connection requires courage—the courage to stand firmly in our truth, to maintain our course even when faced with pressure to compromise, and to recognize that our worth isn't determined by how much we're willing to sacrifice but by how authentically we live our values. Real strength lies not in proving ourselves through endless sacrifice but in maintaining our integrity while remaining open to genuine connection. It's about understanding that true partnership enhances rather than diminishes each person's individual journey. As I continue to explore these themes, I'm reminded that the most profound relationships in my life have been those where both parties remained committed to their personal growth while supporting each other's evolution. These connections weren't built on dependency or sacrifice but on mutual respect, shared values, and a commitment to individual and collective growth. Moving forward, my focus remains on fostering connections that honor both individual sovereignty and collective growth. It's about creating spaces where authenticity is celebrated, boundaries are respected, and growth is mutual. This approach requires wisdom to distinguish between genuine giving and self-diminishing sacrifice, between healthy compromise and harmful concession. The journey toward authentic connection begins with self-respect and continues through the daily practice of honoring both our own truth and the autonomy of others. It's a path that demands courage, wisdom, and an unwavering commitment to personal integrity. As we walk this path, let us remember that our greatest gift to others is not our sacrifice but our authenticity. When we stand firmly in our truth while remaining open to meaningful connection, we create the foundation for relationships that enrich rather than diminish, that elevate rather than constrain. The road continues, and with each step, we learn to balance independence with interconnection, self-respect with genuine care for others. This is the essence of true relationship wisdom—not losing ourselves in others, but finding ourselves through authentic connection while maintaining our individual paths. Still, the journey continues... The journey of self-discovery often leads us to confront uncomfortable truths about human relationships and personal worth. As we navigate the complexities of connection and identity, we must learn to balance caring for others with maintaining our own sense of self. Through my experiences, I've observed a common pattern that transcends gender and culture: the tendency to lose ourselves in the pursuit of approval and acceptance. This pattern often manifests in relationships where one person gradually diminishes their own dreams, aspirations, and well-being in an attempt to prove their worth through perpetual sacrifice. However, true connection—whether in friendship, romance, or professional relationships—flourishes not through self-abandonment but through mutual growth and respect. The most enduring bonds are forged between individuals who maintain their distinct identities while supporting each other's evolution. Consider the paradox of personal value: the more we compromise our core principles and abandon our path in an attempt to please others, the more we undermine the very qualities that make us worthy partners in any relationship. Our unique perspectives, ambitions, and boundaries aren't obstacles to meaningful connections—they're essential ingredients. I've learned that self-respect isn't selfishness; it's the foundation upon which all healthy relationships are built. When we maintain our personal standards, pursue our goals, and honor our boundaries, we demonstrate not just self-love but also respect for others. We show that we value authenticity over approval, substance over superficial acceptance. The path to genuine connection requires courage—the courage to stand firmly in our truth, to maintain our course even when faced with pressure to compromise, and to recognize that our worth isn't determined by how much we're willing to sacrifice but by how authentically we live our values. Real strength lies not in proving ourselves through endless sacrifice but in maintaining our integrity while remaining open to genuine connection. It's about understanding that true partnership enhances rather than diminishes each person's individual journey. As I continue to explore these themes, I'm reminded that the most profound relationships in my life have been those where both parties remained committed to their personal growth while supporting each other's evolution. These connections weren't built on dependency or sacrifice but on mutual respect, shared values, and a commitment to individual and collective growth. Moving forward, my focus remains on fostering connections that honor both individual sovereignty and collective growth. It's about creating spaces where authenticity is celebrated, boundaries are respected, and growth is mutual. This approach requires wisdom to distinguish between genuine giving and self-diminishing sacrifice, between healthy compromise and harmful concession. The journey toward authentic connection begins with self-respect and continues through the daily practice of honoring both our own truth and the autonomy of others. It's a path that demands courage, wisdom, and an unwavering commitment to personal integrity. As we walk this path, let us remember that our greatest gift to others is not our sacrifice but our authenticity. When we stand firmly in our truth while remaining open to meaningful connection, we create the foundation for relationships that enrich rather than diminish, that elevate rather than constrain. The road continues, and with each step, we learn to balance independence with interconnection, self-respect with genuine care for others. This is the essence of true relationship wisdom—not losing ourselves in others, but finding ourselves through authentic connection while maintaining our individual paths. Still, the journey continues...

The Long Road Home: A Story of Loss, Learning, and Renaissance - PART 3

দ্য লং রোড হোম: এ স্টোরি অফ লস, লার্নিং এবং রেনেসাঁ - পার্ট 3

El largo camino a casa: Una historia de pérdida, aprendizaje y renacimiento - PARTE 3

The Long Road Home: A Story of Loss, Learning, and Renaissance - 3. ZATIA

長い帰路: 喪失、学び、そして復興の物語 - パート 3

The Long Road Home: Uma história de perda, aprendizado e renascimento - PARTE 3

Wasi Karu Ñan: Chinkaymanta, Yachaymanta, Renacimientomanta Willakuy - 3 PARTE

Rruga e gjatë në shtëpi: Një histori e humbjes, mësimit dhe rilindjes - PJESA 3

Дуг пут кући: прича о губитку, учењу и ренесанси - 3. ДЕО

நீண்ட சாலை வீடு: இழப்பு, கற்றல் மற்றும் மறுமலர்ச்சியின் கதை - பகுதி 3

ነዊሕ መንገዲ ገዛ፡ ዛንታ ክሳራ፡ ትምህርትን ህዳሴን - 3ይ ክፋል

Uyga uzoq yo'l: Yo'qotish, o'rganish va Uyg'onish hikoyasi - 3-QISM

Ikhaya Lomgwaqo Omude: Indaba Yokulahlekelwa, Ukufunda, kanye Nokuvuselelwa Kabusha - INGXENYE 3

We Analyzed 20.2 Million CI Jobs In Trunk Flaky Tests: Here's What We Learned - Part 1

How to Learn Web Development: HTML, CSS, MERN, and Python - 22 GitHub Repositories to Help You

In today’s article, I’m going to show you a simple, free tool that can help you create great prompts for any AI model you use — whether it’s ChatGPT, Midjourney, or something else. How to Access the Free AI Prompt Generator To get started, you need to go to the website platform.openai.com. If you already have a ChatGPT account, just log in — otherwise, you’ll need to sign up. Once logged in, head over to the OpenAI developer platform. Even if it’s free, you must add your credit card details to access the generator. At the top right corner, you’ll see an option for “Playground.” Click on that, and then choose “System Instructions.” After that, click on the “Generate” button. That’s it! You’re now ready to start creating prompts. Click on that, and then choose “System Instructions.” After that, click on the “Generate” button. That’s it! You’re now ready to start creating prompts. Basically, all you need to do is provide some basic instructions to the model, and it will generate a more complex prompt for you. For instance, let’s say you want to create instructions for YouTube title generation. You can write simple instructions, and the AI will turn them into detailed prompts that you can use for ChatGPT or another AI tool. A Quick Demo Let me show you an example. I wrote a simple instruction, and the model gave me a really detailed prompt that I could use for ChatGPT, Claude, Perplexity, or any other AI. I copied that prompt and pasted it into ChatGPT — and it worked perfectly! The AI followed my instructions exactly how I wanted. https://youtu.be/q7-fQpJunrY?si=ejOs7R9hLbxxeGoe&embedable=true Watch on YouTube: Free AI Prompt Generator The best part is that this prompt generator can be used for so many different tools. You can use it for Midjourney, Leonardo, DALL-E, and more. It’s beneficial if you’re trying to create highly detailed prompts for generating images, code, or other types of content. For example, I used it to create a prompt for Midjourney, and the level of detail was amazing. It gave me all the specific instructions that I wanted the AI to follow. I could use the generated prompt in Midjourney or any other image-generation tool, and the result was really good. If you’re a developer, you can also use this tool to get code examples for your projects. Comparing Different Models One cool feature of this platform is that you can compare the responses from different models that ChatGPT supports. It’s a handy way to see how each model interprets your prompt, so you can choose the best one for your needs. Just remember that while the prompt generator itself is free, the actual requests you make to the model might cost credits, depending on your usage. Conclusion It’s a really simple tool, but it can make a big difference in the quality of your AI prompts. Whether you’re generating images, writing code, or just trying to get better responses from AI, this free prompt generator is worth checking out. If you found this helpful, please click like, share your feedback in the comments below, and don’t forget to subscribe to my YouTube channel. Cheers! ;) In today’s article, I’m going to show you a simple, free tool that can help you create great prompts for any AI model you use — whether it’s ChatGPT, Midjourney, or something else. How to Access the Free AI Prompt Generator To get started, you need to go to the website platform.openai.com . If you already have a ChatGPT account, just log in — otherwise, you’ll need to sign up. Once logged in, head over to the OpenAI developer platform. platform.openai.com platform.openai.com Even if it’s free, you must add your credit card details to access the generator. At the top right corner, you’ll see an option for “Playground.” Click on that, and then choose “System Instructions.” After that, click on the “Generate” button. That’s it! You’re now ready to start creating prompts. Click on that, and then choose “System Instructions.” After that, click on the “Generate” button. That’s it! You’re now ready to start creating prompts. Basically, all you need to do is provide some basic instructions to the model, and it will generate a more complex prompt for you. For instance, let’s say you want to create instructions for YouTube title generation. You can write simple instructions, and the AI will turn them into detailed prompts that you can use for ChatGPT or another AI tool. A Quick Demo Let me show you an example. I wrote a simple instruction, and the model gave me a really detailed prompt that I could use for ChatGPT, Claude, Perplexity, or any other AI. I copied that prompt and pasted it into ChatGPT — and it worked perfectly! The AI followed my instructions exactly how I wanted. https://youtu.be/q7-fQpJunrY?si=ejOs7R9hLbxxeGoe&embedable=true https://youtu.be/q7-fQpJunrY?si=ejOs7R9hLbxxeGoe&embedable=true Watch on YouTube: Free AI Prompt Generator Watch on YouTube: Free AI Prompt Generator Free AI Prompt Generator Free AI Prompt Generator The best part is that this prompt generator can be used for so many different tools. You can use it for Midjourney, Leonardo, DALL-E, and more. It’s beneficial if you’re trying to create highly detailed prompts for generating images, code, or other types of content. For example, I used it to create a prompt for Midjourney, and the level of detail was amazing. It gave me all the specific instructions that I wanted the AI to follow. I could use the generated prompt in Midjourney or any other image-generation tool, and the result was really good. If you’re a developer, you can also use this tool to get code examples for your projects. Comparing Different Models One cool feature of this platform is that you can compare the responses from different models that ChatGPT supports. It’s a handy way to see how each model interprets your prompt, so you can choose the best one for your needs. Just remember that while the prompt generator itself is free, the actual requests you make to the model might cost credits, depending on your usage. Conclusion It’s a really simple tool, but it can make a big difference in the quality of your AI prompts. Whether you’re generating images, writing code, or just trying to get better responses from AI, this free prompt generator is worth checking out. If you found this helpful, please click like, share your feedback in the comments below, and don’t forget to subscribe to my YouTube channel . YouTube channel YouTube channel Cheers! ;)

Learn How to Access and Use OpenAI's Free AI Prompt Generator

Freedom has long been humanity's most persistent pursuit, a flame that has illuminated civilizations, revolutions, and personal transformations. My own journey reflects this quest, marked by achievements, setbacks, and profound realizations about the true essence of freedom. Throughout my professional life, I’ve embraced a lifestyle that many would envy, enjoying the ability to work from anywhere at any time. This seemed like the ultimate expression of liberty. However, I’ve come to understand that external mobility does not equate to internal freedom. True sovereignty is far more nuanced and complex than mere geographical independence. One of the most painful lessons I’ve encountered is the danger of over-attachment. We often become enslaved not by external forces, but by our own desires, obsessions, and expectations. When something we deeply care about is suddenly taken away, we find ourselves vulnerable, our sense of self shattered. This, I realized, is not freedom; it is a bondage of the most insidious kind. True sovereignty lies in cultivating a sense of self that remains unbroken, regardless of external circumstances. It involves recognizing that our worth is not defined by what we own, but by our capacity to adapt, to rise, and to transform in the face of adversity. In my understanding, true freedom rests on three fundamental aspects of life. First is the sovereignty of time, which allows me to choose how I invest my most precious resource. Second is the sovereignty of location, granting me the freedom to shape my environment rather than being constrained by it. Lastly, there is the sovereignty of financial aspiration, empowering me to design my economic destiny without being limited by traditional constraints. To deepen my understanding of freedom, I have delved into historical narratives of civilizations and individuals who have pursued sovereignty. From the philosophical resistance of the Stoics to the revolutionary spirits of indigenous cultures, and from the intellectual rebellions of enlightenment thinkers to the economic pioneers of our modern era, each story offers valuable insights into the nature of true liberation. These historical journeys reveal that freedom is not a destination but a continuous process of learning, unlearning, and redefining our boundaries. Ironically, the path to sovereignty requires a profound understanding of what we can and cannot control. It demands a delicate balance between ambition and acceptance, between striving and surrendering. We must learn to hold our dreams lightly, pursuing them with passion but not desperation. Currently, my life’s trajectory is focused not on conquering external worlds but on exploring the intricate landscapes of human potential. It involves discovering how individuals can craft lives filled with meaning, impact, and genuine freedom. This journey is not solitary; it is a collective exploration. It is about connecting with fellow travelers who understand that true sovereignty is not about standing alone but about creating meaningful connections that amplify both individual and collective potential. As I navigate this path, I am guided by a simple yet profound understanding: freedom is not a state to be achieved but a continuous dance of adaptation, learning, and growth. And so, the journey continues... Freedom has long been humanity's most persistent pursuit, a flame that has illuminated civilizations, revolutions, and personal transformations. My own journey reflects this quest, marked by achievements, setbacks, and profound realizations about the true essence of freedom. Throughout my professional life, I’ve embraced a lifestyle that many would envy, enjoying the ability to work from anywhere at any time. This seemed like the ultimate expression of liberty. However, I’ve come to understand that external mobility does not equate to internal freedom. True sovereignty is far more nuanced and complex than mere geographical independence. One of the most painful lessons I’ve encountered is the danger of over-attachment. We often become enslaved not by external forces, but by our own desires, obsessions, and expectations. When something we deeply care about is suddenly taken away, we find ourselves vulnerable, our sense of self shattered. This, I realized, is not freedom; it is a bondage of the most insidious kind. True sovereignty lies in cultivating a sense of self that remains unbroken, regardless of external circumstances. It involves recognizing that our worth is not defined by what we own, but by our capacity to adapt, to rise, and to transform in the face of adversity. In my understanding, true freedom rests on three fundamental aspects of life. First is the sovereignty of time, which allows me to choose how I invest my most precious resource. Second is the sovereignty of location, granting me the freedom to shape my environment rather than being constrained by it. Lastly, there is the sovereignty of financial aspiration, empowering me to design my economic destiny without being limited by traditional constraints. To deepen my understanding of freedom, I have delved into historical narratives of civilizations and individuals who have pursued sovereignty. From the philosophical resistance of the Stoics to the revolutionary spirits of indigenous cultures, and from the intellectual rebellions of enlightenment thinkers to the economic pioneers of our modern era, each story offers valuable insights into the nature of true liberation. These historical journeys reveal that freedom is not a destination but a continuous process of learning, unlearning, and redefining our boundaries. Ironically, the path to sovereignty requires a profound understanding of what we can and cannot control. It demands a delicate balance between ambition and acceptance, between striving and surrendering. We must learn to hold our dreams lightly, pursuing them with passion but not desperation. Currently, my life’s trajectory is focused not on conquering external worlds but on exploring the intricate landscapes of human potential. It involves discovering how individuals can craft lives filled with meaning, impact, and genuine freedom. This journey is not solitary; it is a collective exploration. It is about connecting with fellow travelers who understand that true sovereignty is not about standing alone but about creating meaningful connections that amplify both individual and collective potential. As I navigate this path, I am guided by a simple yet profound understanding: freedom is not a state to be achieved but a continuous dance of adaptation, learning, and growth. And so, the journey continues...

The Long Road Home: A Story of Loss, Learning, and Renaissance - PART 2

Die Langpad Huistoe: 'n Verhaal van verlies, leer en Renaissance - DEEL 2

Der lange Weg nach Hause: Eine Geschichte von Verlust, Lernen und Renaissance – TEIL 2

El largo camino a casa: Una historia de pérdida, aprendizaje y renacimiento - PARTE 2

הדרך הארוכה הביתה: סיפור על אובדן, למידה ורנסנס - חלק 2

長い帰路: 喪失、学び、そして復興の物語 - パート 2

The Long Road Home: Kisah Kehilangan, Pembelajaran dan Renaissance - BAHAGIAN 2

د لونګ سړک کور: د لاسه ورکولو کیسه، زده کړه، او بیا رغونه - 2 برخه

Wasi Karu Ñan: Chinkaymanta, Yachaymanta, Renacimientomanta Willakuy - 2 kaq PARTE

Dlhá cesta domov: Príbeh o strate, učení a renesancii - ČASŤ 2

Роҳи дароз ба хона: Қиссаи талафот, омӯзиш ва эҳё - ҚИСМИ 2

Uzyn ýol öýi: ossitirmek, öwrenmek we galkynyş hakda hekaýa - 2-nji BÖLÜM

The Long Road Home: A Story of Loss, Learning, and Renaissance - BAHAGI 2

Cryptocurrency has gained increased attention as the financial landscape transforms, particularly in the growing field of digital assets. More than one-fifth of men (21%) hold cryptocurrencies, nearly twice that of women (11%). However, 46% of women don’t disprove interest in cryptocurrencies, narrowing the gap compared to men, 54% of whom answered identically. Among those potentially interested, half of the men surveyed would choose to pursue professional education or take a course as a first step before making any purchases. Interestingly, women show even greater openness to learning, with 54% opting to start with professional education. This data comes from an international survey conducted by BITmarkets Academy in partnership with Ipsos, which included over 1,550 respondents from selected European markets. “The survey highlights that, as with other areas of investment, men are more proactive in the cryptocurrency space, more willing to take risks, and more inclined to try new technologies. However, it is important to note that women's potential interest in cryptocurrencies is nearly on par with men's, and women are even more inclined to study the field before diving in. This may signal a trend toward converging economic interests between men and women,” said Ali Daylami, Head of Data Analytics at BITmarkets, commenting on the survey's findings. Men also report a stronger understanding of basic cryptocurrency terms, with only 4% admitting they know nothing about cryptocurrencies, compared to 9% of women. When it comes to more technical concepts such as “cryptocurrency exchange,” 14% of men and 24% of women admit a lack of knowledge. In the case of ETFs (exchange-traded funds), which increasingly invest in cryptocurrencies, ignorance rises to 29% among men and 41% among women. While statistics show that women generally hold less cryptocurrency compared to men, research shows that women are more open to learning about cryptocurrencies. This points to a growing curiosity and potential for greater involvement in the crypto space, as education and awareness are important in overcoming barriers such as financial confidence. By addressing these challenges, the cryptocurrency industry has the opportunity to engage and empower a broader, more diverse group of investors and participants. The full survey report is available at BITmarkets Academy. BITmarkets Academy is an educational project of the international cryptocurrency exchange BITmarkets. The exchange offers 24/7 support in 15 languages. Traders can engage in trading more than 200 cryptocurrencies, as well as gain access to daily market updates and diverse educational materials. For more information, explore BITmarkets.com or visit the exchange’s listing on CoinMarketCap.com. Cryptocurrency has gained increased attention as the financial landscape transforms, particularly in the growing field of digital assets. More than one-fifth of men (21%) hold cryptocurrencies, nearly twice that of women (11%). However, 46% of women don’t disprove interest in cryptocurrencies, narrowing the gap compared to men, 54% of whom answered identically. Among those potentially interested, half of the men surveyed would choose to pursue professional education or take a course as a first step before making any purchases. Interestingly, women show even greater openness to learning, with 54% opting to start with professional education. This data comes from an international survey conducted by BITmarkets Academy in partnership with Ipsos, which included over 1,550 respondents from selected European markets. BITmarkets Academy “The survey highlights that, as with other areas of investment, men are more proactive in the cryptocurrency space, more willing to take risks, and more inclined to try new technologies. However, it is important to note that women's potential interest in cryptocurrencies is nearly on par with men's, and women are even more inclined to study the field before diving in. This may signal a trend toward converging economic interests between men and women,” said Ali Daylami, Head of Data Analytics at BITmarkets, commenting on the survey's findings. Men also report a stronger understanding of basic cryptocurrency terms, with only 4% admitting they know nothing about cryptocurrencies, compared to 9% of women. When it comes to more technical concepts such as “cryptocurrency exchange,” 14% of men and 24% of women admit a lack of knowledge. In the case of ETFs (exchange-traded funds), which increasingly invest in cryptocurrencies, ignorance rises to 29% among men and 41% among women. While statistics show that women generally hold less cryptocurrency compared to men, research shows that women are more open to learning about cryptocurrencies. This points to a growing curiosity and potential for greater involvement in the crypto space, as education and awareness are important in overcoming barriers such as financial confidence. By addressing these challenges, the cryptocurrency industry has the opportunity to engage and empower a broader, more diverse group of investors and participants. The full survey report is available at BITmarkets Academy. The full survey report is available at BITmarkets Academy . BITmarkets Academy BITmarkets Academy is an educational project of the international cryptocurrency exchange BITmarkets. The exchange offers 24/7 support in 15 languages. Traders can engage in trading more than 200 cryptocurrencies, as well as gain access to daily market updates and diverse educational materials. For more information, explore BITmarkets.com or visit the exchange’s listing on CoinMarketCap.com. BITmarkets Academy BITmarkets Academy BITmarkets Academy is an educational project of the international cryptocurrency exchange BITmarkets. The exchange offers 24/7 support in 15 languages. Traders can engage in trading more than 200 cryptocurrencies, as well as gain access to daily market updates and diverse educational materials. For more information, explore BITmarkets.com or visit the exchange’s listing on CoinMarketCap.com . BITmarkets.com CoinMarketCap.com

Women Are More Eager to Learn About Cryptocurrency, Even if They Own Less

মহিলারা ক্রিপ্টোকারেন্সি সম্পর্কে জানতে বেশি আগ্রহী, যদিও তাদের মালিকানা কম

Žene su više željne saznanja o kriptovalutama, čak i ako posjeduju manje

Las mujeres están más dispuestas a aprender sobre criptomonedas, incluso si poseen menos

Emakumeak irrikaz daude Kriptomoneta buruz ikasteko, nahiz eta gutxiago eduki

Naiset ovat innokkaampia oppimaan kryptovaluutoista, vaikka he omistaisivat vähemmän

Կանայք ավելի շատ են ցանկանում սովորել կրիպտոարժույթի մասին, նույնիսկ եթե նրանք ավելի քիչ ունեն

女性は保有する仮想通貨が少ないにもかかわらず、仮想通貨について学ぶことに熱心である

여성들은 암호화폐에 대해 더 알고 싶어하지만, 암호화폐를 덜 소유하고 있어도 더 열망합니다.

Basi Bazali Na Posa Ya Koyeba Cryptocurrency, Ata Soki Bazali Na Moke

Sievietes vairāk vēlas uzzināt par kriptovalūtu, pat ja viņām pieder mazāk

කාන්තාවන් අඩුවෙන් හිමි වුවද, Cryptocurrency ගැන ඉගෙන ගැනීමට වැඩි කැමැත්තක් දක්වයි

Ayollar kamroq egalik qilishsa ham, kriptovalyuta haqida ko'proq o'rganishni xohlashadi

Learn How Build a Progressive Web App in Minutes with Next.js

Obteniu informació sobre com crear una aplicació web progressiva en qüestió de minuts amb Next.js

Aprenda a crear una aplicación web progresiva en minutos con Next.js

Découvrez comment créer une application Web progressive en quelques minutes avec Next.js

Next.js के साथ मिनटों में प्रगतिशील वेब ऐप बनाना सीखें

Naučite kako izgraditi progresivnu web-aplikaciju u nekoliko minuta uz Next.js

Pelajari Cara Membuat Aplikasi Web Progresif dalam Hitungan Menit dengan Next.js

Scopri come creare un'app Web progressiva in pochi minuti con Next.js

Next.js を使って数分でプログレッシブ Web アプリを構築する方法を学ぶ

Leer hoe u in enkele minuten een progressieve web-app bouwt met Next.js

Aflați cum să creați o aplicație web progresivă în câteva minute cu Next.js

Научите како да направите прогресивну веб апликацију за неколико минута помоћу Нект.јс

Next.js yordamida bir necha daqiqada progressiv veb-ilovani qanday yaratishni bilib oling

Stories, News & Mentions about LEAR

Lear’s Stories on HackerNoon

Lear's News & Mentions