Onsemi has been working with advisers in recent months to pursue Allegro, the Bloomberg report said, adding that it's possible other suitors could emerge for Allegro and the company hasn't indicated whether it would be open to a potential sale. Semiconductor companies interested in bulking ...

Allegro drawing takeover interest from ON Semiconductor, Bloomberg News reports

We recently published an article titled These 10 Companies Led Monday’s Downturn. In this article, we are going to take a look at where ON Semiconductor Corporation (NASDAQ:ON) stands against the other stocks. Wall Street’s major indices kicked off the trading on a positive note, all finishing ...

Why ON Semiconductor Corporation (ON) Went Down on Monday

Semiconductor stocks moving on news Monday included Arm Holdings, Micron Technology and Taiwan Semiconductor Manufacturing.

Semiconductor Stocks Arm, Micron, TSMC Advance On Positive News | Investor's Business Daily

The Biden administration said Monday it plans to send $6.6 billion in grants to the Taiwan Semiconductor Manufacturing Company, while the chipmaking giant said it would expand its footprint in Arizona.

TSMC gets $6.6 billion in chipmaking cash from Biden while pledging to build a third Arizona plant

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Taiwan Semiconductor Manufacturing Co. will build a third chipmaking fab or factory in north Phoenix, spurred on by a grant worth up to $6.6 billion.

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The company&#x27;s sales data confirms a recovery is in place for the semiconductor industry.

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The US government plans to give $6.6 billion to the world’s biggest manufacturer of semiconductor chips to help it build three factories in Arizona as part of President Joe Biden’s efforts to secure the supply of advanced chips.

TSMC: Biden to give Taiwanese company $6.6 billion to ramp up US chip production | CNN Business

The CHIPS Program Office announced a $6.6 billion grant to TSMC this morning. TSMC will build a third fab and bring its most advanced process to the U.S. This may be a tipping point for Arizona.

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The US has been pushing to expand chip production, citing economic and national security risks.

TSMC wins subsidies to expand US chip manufacturing in Arizona

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Let me tell you my experience 🔎 What is Causality? Traditional statistical and machine learning methods mostly focus on correlations, but causal models allow researchers to infer mechanisms and predict the effects of interventions. Nevertheless, the discovery of cause-and-effect relationships has been always attractive. Indeed, despite being used in many types of data, this is particularly relevant for time series data, for example, the stock market or clinical data as the cerebral activity. Non-time series cases can be based on causal networks of discrete events where one event causes another or more: Flu  →  Fever ↘  →  Cough Rain → Slippery Road → People falling A user Likes Sci-Fi → Recommend him to buy or watch "Interstellar" Those might appear trivial chains, but they can be expanded to many components (also called larger networks) providing insightful inputs that can only be reached computationally given the large amount of data. With the rise of machine learning and deep learning, the need for robust causal inference tools is growing even more compared to the past. Most models from the nearly century-old autoregressors or the more recent zero-shot large foundation models (like TimesFM, CHRONOS, LLMTime)—focus on forecasting values. But they often leave one burning question unanswered: "Does one time series actually influence another?" For example, consider the surge in demand for Nvidia cards. A time series prediction could be able to predict rising stock prices for Nvidia, but what about the ripple effects on semiconductor supplier shares like TSMC? And what if the changes there will be even more pronounced and therefore more relevant (even if caused by another stock share)? That's where causality-driven AI comes into play. 🛠️From Correlation to Causation Emerging from econometrics, the most common approach is the Granger causality. This was introduced by the British economist Clive Granger more than 5 decades ago, and it has been the go-to technique since it has low computation and is therefore easily scalable. It’s simple: if changes in series X help predict series Y after some time, then X is said to “Granger-cause” Y. Here’s a quick Python snippet using the Statsmodels library: import statsmodels.api as sm
from statsmodels.tsa.stattools import grangercausalitytests

# Load sample macroeconomic data
data = sm.datasets.macrodata.load_pandas().data[["realgdp", "realcons"]].pct_change().dropna()
gc_results = grangercausalitytests(data, maxlag=4) The “lag” in the last row is used to indicate how back/far in time we should look for relationships (in brain activity from functional magnetic resonance imaging is generally “1”) While handy, Granger causality is inherently linear. It struggles to capture the non-linear interplay that is so common in complex systems. Obviously, if we are interested in just 2 time series, it is enough. «Complex» means many parts.🚑 🌟 The Stuff Inside EffConPy: EffConPy addresses the aforementioned challenges; it is easily installed by using the command «pip install effconnpy», and it is developed by a team of people led by Dr. Alessandro Crimi from the technical university of Krakow. The library includes multivariate Granger causality, which even if based on Statsmodel, is not out-of-the-box (thank you for putting it together), and more modern approaches such as dynamic Bayesian networks, convergent cross-mapping, etc., which might detect subtle, non-linear-causal-influences-that-traditional models might miss. They are all put together, and therefore, it allows straightforward benchmarks, just changing the method in the last row of this snip, for example: from effconnpy import CausalityAnalyzer
import numpy as np
# Generate sample time series
data = np.random.rand(100, 3)
analyzer = CausalityAnalyzer(data)
results = analyzer.causality_test(method='ccm') If you are wondering why we need something more than Granger causality, let me explain it to you here: Granger causality is explicitly temporal (although some people complain that we have the so-called temporal correlation). It checks whether past values of one variable improve the prediction of another beyond its own past values. However, it assumes linear relationships and does not capture causality in complex dynamical systems where interactions are nonlinear or bidirectional. Dynamic system approaches like Convergent Cross Mapping (CCM) and Transfer Entropy (TE) are focused on those aspects. CCM, based on state-space reconstruction from chaos theory, detects causality by checking if one variable's historical states can reconstruct another’s, implying a shared underlying dynamical system. The math related to this is not just a regression in time. It is based on advanced concepts from the dynamical system of the butterfly effect of the Lorentz attractor: This is nothing esoteric, it is just math particularly useful for feedback systems and ecological interactions. TE, rooted in information theory, quantifies how much knowing one variable reduces uncertainty in another, making it well-suited for detecting nonlinear and asymmetric dependencies. Until now, I have not seen a Python framework putting all this together. 🕵️What I Did NOT Find Inside Effconnpy There has been a growing interest in large foundation models (LFM) for time series prediction, such as TimesFM. Those models are good predictors and they do little work with causality. Therefore, it is understandable that they have been left out by a causal toolbox. However, they are probably the future. Indeed, we need little work to turn them from predictor to causality. Like with Granger, we could check if one series helps predict another one. The other missing thing is reservoir computing causality. A reservoir is not something from your pipe. It is a dynamic, randomly connected network (metaphorically like fluid water), which, in response, to input data, generates intricate patterns. In practice, it is designed by recurrent neural networks. So, after all, it is AI. The patterns created within the «reservoir» help us reveal deeper causal links, such as how fluctuations in one stock market might ripple through related industries or how a stroke disrupts neural connectivity in the brain. This approach was recently introduced in an IEEE Access study titled “End-to-End Stroke Imaging Analysis Using Effective Connectivity and Interpretable Artificial Intelligence” by Wojciech Ciezobka and colleagues, who applied these advanced techniques to healthcare. They used effective connectivity analysis to examine how the activity in one brain region influences another—vital for understanding stroke impacts. It will be great if also this will be included. 📝 Conclusion Causality studies have always been attractive for people interested in time series analysis. By studying the temporal relationships between elements, especially their cause-effect, we allow us to understand better the intricate dynamics that govern our world from the available data, independently if we are talking of economic indicators or the connectivity within the human brain. If you want to know more? As I said, it is open-source and therefore available on the related GitHub repository, or keep the conversation on how causality-driven AI is redefining our understanding of time series data, commenting here.🚀🧠 Let me tell you my experience 🔎 What is Causality? 🔎 What is Causality? Traditional statistical and machine learning methods mostly focus on correlations, but causal models allow researchers to infer mechanisms and predict the effects of interventions. Nevertheless, the discovery of cause-and-effect relationships has been always attractive. Indeed, despite being used in many types of data, this is particularly relevant for time series data, for example, the stock market or clinical data as the cerebral activity. Non-time series cases can be based on causal networks of discrete events where one event causes another or more: Flu  →  Fever Flu  →  Fever ↘  →  Cough ↘  →  Cough Rain → Slippery Road → People falling Rain → Slippery Road → People falling A user Likes Sci-Fi → Recommend him to buy or watch "Interstellar" A user Likes Sci-Fi → Recommend him to buy or watch "Interstellar" Those might appear trivial chains, but they can be expanded to many components (also called larger networks) providing insightful inputs that can only be reached computationally given the large amount of data. With the rise of machine learning and deep learning, the need for robust causal inference tools is growing even more compared to the past. Most models from the nearly century-old autoregressors or the more recent zero-shot large foundation models (like TimesFM , CHRONOS , LLMTime )—focus on forecasting values. But they often leave one burning question unanswered: "Does one time series actually influence another?" TimesFM CHRONOS LLMTime "Does one time series actually influence another?" For example, consider the surge in demand for Nvidia cards. A time series prediction could be able to predict rising stock prices for Nvidia, but what about the ripple effects on semiconductor supplier shares like TSMC? And what if the changes there will be even more pronounced and therefore more relevant (even if caused by another stock share)? That's where causality-driven AI comes into play. 🛠️From Correlation to Causation Emerging from econometrics, the most common approach is the Granger causality . This was introduced by the British economist Clive Granger more than 5 decades ago, and it has been the go-to technique since it has low computation and is therefore easily scalable. It’s simple: if changes in series X help predict series Y after some time, then X is said to “Granger-cause” Y . Granger causality if changes in series X help predict series Y after some time, then X is said to “Granger-cause” Y Here’s a quick Python snippet using the Statsmodels library: import statsmodels.api as sm
from statsmodels.tsa.stattools import grangercausalitytests

# Load sample macroeconomic data
data = sm.datasets.macrodata.load_pandas().data[["realgdp", "realcons"]].pct_change().dropna()
gc_results = grangercausalitytests(data, maxlag=4) import statsmodels.api as sm
from statsmodels.tsa.stattools import grangercausalitytests

# Load sample macroeconomic data
data = sm.datasets.macrodata.load_pandas().data[["realgdp", "realcons"]].pct_change().dropna()
gc_results = grangercausalitytests(data, maxlag=4) The “lag” in the last row is used to indicate how back/far in time we should look for relationships (in brain activity from functional magnetic resonance imaging is generally “1”) While handy, Granger causality is inherently linear. It struggles to capture the non-linear interplay that is so common in complex systems. Obviously, if we are interested in just 2 time series, it is enough. «Complex» means many parts.🚑 🌟 The Stuff Inside EffConPy: EffConPy addresses the aforementioned challenges; it is easily installed by using the command «pip install effconnpy», and it is developed by a team of people led by Dr. Alessandro Crimi from the technical university of Krakow. The library includes multivariate Granger causality, which even if based on Statsmodel, is not out-of-the-box (thank you for putting it together), and more modern approaches such as dynamic Bayesian networks, convergent cross-mapping, etc., which might detect subtle, non-linear-causal-influences-that-traditional models might miss. They are all put together, and therefore, it allows straightforward benchmarks, just changing the method in the last row of this snip, for example: from effconnpy import CausalityAnalyzer
import numpy as np
# Generate sample time series
data = np.random.rand(100, 3)
analyzer = CausalityAnalyzer(data)
results = analyzer.causality_test(method='ccm') from effconnpy import CausalityAnalyzer
import numpy as np
# Generate sample time series
data = np.random.rand(100, 3)
analyzer = CausalityAnalyzer(data)
results = analyzer.causality_test(method='ccm') If you are wondering why we need something more than Granger causality, let me explain it to you here: Granger causality is explicitly temporal (although some people complain that we have the so-called temporal correlation). It checks whether past values of one variable improve the prediction of another beyond its own past values. However, it assumes linear relationships and does not capture causality in complex dynamical systems where interactions are nonlinear or bidirectional. Dynamic system approaches like Convergent Cross Mapping (CCM) and Transfer Entropy (TE) are focused on those aspects. CCM, based on state-space reconstruction from chaos theory, detects causality by checking if one variable's historical states can reconstruct another’s, implying a shared underlying dynamical system. The math related to this is not just a regression in time. It is based on advanced concepts from the dynamical system of the butterfly effect of the Lorentz attractor: Convergent Cross Mapping (CCM) Transfer Entropy (TE) This is nothing esoteric, it is just math particularly useful for feedback systems and ecological interactions. TE, rooted in information theory, quantifies how much knowing one variable reduces uncertainty in another, making it well-suited for detecting nonlinear and asymmetric dependencies. Until now, I have not seen a Python framework putting all this together. 🕵️ What I Did NOT Find Inside Effconnpy 🕵️ What I Did NOT Find Inside Effconnpy NOT There has been a growing interest in large foundation models (LFM) for time series prediction, such as TimesFM . Those models are good predictors and they do little work with causality. Therefore, it is understandable that they have been left out by a causal toolbox. However, they are probably the future. Indeed, we need little work to turn them from predictor to causality. Like with Granger, we could check if one series helps predict another one. TimesFM The other missing thing is reservoir computing causality. A reservoir is not something from your pipe. It is a dynamic, randomly connected network (metaphorically like fluid water), which, in response, to input data, generates intricate patterns. In practice, it is designed by recurrent neural networks. So, after all, it is AI. reservoir computing causality. The patterns created within the «reservoir» help us reveal deeper causal links, such as how fluctuations in one stock market might ripple through related industries or how a stroke disrupts neural connectivity in the brain. This approach was recently introduced in an IEEE Access study titled “End-to-End Stroke Imaging Analysis Using Effective Connectivity and Interpretable Artificial Intelligence” by Wojciech Ciezobka and colleagues , who applied these advanced techniques to healthcare. They used effective connectivity analysis to examine how the activity in one brain region influences another—vital for understanding stroke impacts. It will be great if also this will be included. IEEE Access study titled “End-to-End Stroke Imaging Analysis Using Effective Connectivity and Interpretable Artificial Intelligence” by Wojciech Ciezobka and colleagues “End-to-End Stroke Imaging Analysis Using Effective Connectivity and Interpretable Artificial Intelligence” 📝 Conclusion Causality studies have always been attractive for people interested in time series analysis. By studying the temporal relationships between elements, especially their cause-effect, we allow us to understand better the intricate dynamics that govern our world from the available data, independently if we are talking of economic indicators or the connectivity within the human brain. If you want to know more? As I said, it is open-source and therefore available on the related GitHub repository , or keep the conversation on how causality-driven AI is redefining our understanding of time series data, commenting here.🚀🧠 the related GitHub repository

EffConPy: Open Source Causality Discovery in Python

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USDA Farmers' Bulletin No. 2277: Computers on the Farm, by Deborah Takiff Smith is part of the HackerNoon Books Series. You can jump to any chapter in this book here .
 Glossary of Computer Terms Glossary of Computer Terms Listed below are some of the shorthand or jargon terms in the computer field. Understanding these terms will help you discuss hardware and software systems and their operation. A number specifying a particular location in the computer's memory. ADDRESS: : A relatively easy-to-use computer language that comes with most small and personal computer systems. BASIC (Beginner's All-purpose Symbolic Instruction Code) The speed at which information is exchanged over communications lines, generally expressed in characters per second. 300 baud is the most common rate. It is equivalent to 30 characters per second. BAUD RATE: A two-digit numbering system based on the digits 0 and 1. It is the basis for calculations on all computers, and the basis for storing and retrieving information, including alphabet characters. BINARY: The smallest unit of information the computer recognizes. A bit is represented by the presence or absence of an electronic pulse, 0 or 1. BIT: A fault or error in a computer program. BUG: A byte is composed of several bits, and is used to represent one character—such as a letter, number, or punctuation mark. The older microcomputer systems used 8 bits per byte, but the newer ones are based on 16 or 32 bits per byte. BYTE: A thin silicon wafer on which electronic components are deposited lithographically in the form of integrated circuits. CHIP: : A high-level programing language widely used in business applications. COBOL (Common Business-Oriented Language) : Two or more computers that are connected so they can exchange information. COMPUTER NETWORK : A collection of instructions that together direct the computer to perform a particular function. COMPUTER PROGRAM : A popular operating system for small computers. CP/M (Control Program for Microprocessors) : The part of the computer that controls and organizes the operations of the other parts of the computer and does the calculations. CPU (Central Processing Unit) « 35 » : A video screen that can be used for viewing output. CRT (Cathode Ray Tube) : The information, such as numbers or letters, that are put into the computer system. DATA : To remove the errors in a computer program. DEBUG : A program for detecting and isolating a problem or mistake in the computer system; features that allow systems or equipment to self-test for flaws. DIAGNOSTIC : A revolving plate on which data and programs are stored. Also called DISKETTE. DISK : A part of the computer system that reads and writes material on the disk. It can be part of the main hardware or a peripheral attached to the system. DISK DRIVE : 1. The instruction manual for a program (software) or piece of hardware. 2. The process of describing a computer program so others using the program can see how it works. DOCUMENTATION : Any time a computer is not available or not working because of a machine fault or failure. Downtime includes repair delay time, repair time, and machine-spoiled work time. DOWNTIME : To change or add data to an existing document or program. EDIT : A small, flexible storage device made of magnetic material. It looks like a soft phonograph record and is usually 5¼ inches or 8 inches in diameter. FLOPPY DISK : A computer language widely used to solve scientific and engineering problems, mainly for large commercial systems. FORTRAN (FORmula TRANslation) : Meaningless information. GARBAGE : A printout on paper of information from the computer. HARD COPY : All the physical parts of the computer system, including the computer itself, the input and output equipment and peripherals, and the physical disk or tape equipment. (The computer programs are software.) HARDWARE : The data that are put into the computer, or the process of putting it in. INPUT : A group of bits that designates a specific computer operation. INSTRUCTION : An electronic circuit or combination of circuits contained on semiconductor material, or chip. INTEGRATED CIRCUIT : A computer system that allows two-way communication between the user and the computer. INTERACTIVE « 36 » : A piece of equipment used to connect two parts of a computer system that cannot interact directly with each other. INTERFACE : A measure of computer memory capacity. Each K of information is 1,024 bytes. K (kilobyte) : To put data or programs into a computer. LOAD : A recording device used to store programs and data. It resembles audio tape used in tape recorders. MAGNETIC TAPE : That part of the computer that stores information. Also, the external material, such as floppy disks, hard disks, or cassette tapes that store information. MEMORY : A small computer in which the CPU is an integrated circuit deposited on a silicon chip. MICROCOMPUTER : A silicon chip that is the central, controlling part of the computer. MICROPROCESSOR : A computer that is usually larger, more powerful, and more expensive than a microcomputer, but is smaller than a mainframe in memory and functions. MINICOMPUTER : A device used to attach a computer or one of its devices to a communication line, often a telephone. MODEM (MODulator/ DEModulator) : A special group of programs which controls the overall operation of a computer system. It mediates between the hardware and the particular software program. OPERATING SYSTEM : The information generated by a computer. OUTPUT : A device, such as a CRT, disk drive, or printer, used for entering or storing data into, or retrieving it from, the computer system. PERIPHERAL : An output device to print the information from a computer. PRINTER : A set of coded instructions directing a computer to perform a particular function. PROGRAM : A special language of words and rules that is used to write programs so the computer can understand them. PROGRAMING LANGUAGE : The portion of the computer's memory in which data, instructions, and other information are stored temporarily. Also called read-write memory. RAM (Random Access Memory) : The portion of the computer's memory that contains information and instructions that are stored permanently. This memory cannot be altered or added to. ROM (Read Only Memory) « 37 » : A material such as silicon with a conductivity between that of a metal and an insulator. It is used in the manufacture of solid-state devices such as diodes, transistors, and the complex integrated circuits that comprise computer logic circuits. SEMICONDUCTOR : A general term for computer programs, procedural rules, and sometimes the documentation involved in the operation of a computer. SOFTWARE : The computer and all its related components, including hardware and software, that work together. SYSTEM : A peripheral device through which information is entered into or extracted from the computer, usually with a keyboard and an output device such as a CRT or printer. TERMINAL : A method by which more than one person can use a computer at the same time at separate terminals. TIMESHARING : A computer system that has all hardware and software installed. Supposedly, all you have to do is turn it on. TURNKEY SYSTEM : Typing, editing, storing, and printing text with a computer. WORD PROCESSING The mention of commercial products, services, or companies does not constitute endorsement by the U.S. Department of Agriculture. If additional computer services of interest to the agricultural community are available, we would be glad to consider them for inclusion in possible revisions of this bulletin. About HackerNoon Book Series: We bring you the most important technical, scientific, and insightful public domain books. This book is part of the public domain. Smith, Deborah Takiff. 2019. USDA Farmers: Bulletin No. 2277: Computer on the Farm. Urbana, Illinois: Project Gutenberg. Retrieved May 2022 from https://www.gutenberg.org/files/59316/59316-h/59316-h.htm This eBook is for the use of anyone anywhere at no cost and with almost no restrictions whatsoever. You may copy it, give it away or re-use it under the terms of the Project Gutenberg License included with this eBook or online at www.gutenberg.org , located at https://www.gutenberg.org/policy/license.html .

Computers on the Farm: Glossary of Computer Terms

Artificial intelligence is perhaps the only technology that, without exaggeration, can be described as a "game-changer." From unlocking the natural world's mysteries to turning everyday objects like drones into smart weapons, grant enormous power to countries and companies that control and use them. And it seems only natural that the newly elected President of the United States is seeking to cement America's position as the world's leading AI superpower. In this article, we'll investigate the four major areas where applies and how the new policies will affect the governmental structures, local businesses, and ordinary Americans. artificial intelligence solutions Biden's national AI strategy A sneak peek of Joe Biden's national AI strategy: here's what to expect "Continued American leadership in AI is of paramount importance to maintaining the economic and national security of the United States and to shaping the global evolution of AI in a manner consistent with our Nation's values, policies, and priorities." President Donald J. Trump, February 11, 2019 Although the Trump administration launched the first-ever national AI strategy a few years earlier, it was not until Joe Biden took office that federal AI spending reached a historic high. Through 2021, Washington $6 billion into AI-related R&D projects and initiatives. The country's renewed interest in artificial intelligence is largely ignited by Biden's plan to challenge China and its views on AI usage. In the spring of 2022, the National Artificial Intelligence Research Task Force an interim report highlighting the national artificial intelligence strategy and implementation plan, with the full report expected later that year. Experts believe that the US AI strategy will cover complex scientific, economic, social, and security issues of artificial intelligence and the associated technologies, such as data storage and processing platforms. could invest is set to present The government wants America to lead the artificial intelligence race A 2020 indicates that 82% of Americans "are somewhat or very concerned" about other countries being more advanced in AI than the United States. And there's no smoke without fire.Although the United States is still leading the artificial intelligence race, China boasts superior monitoring and surveillance technologies, which are often used to suppress its citizens and control other countries' information flows.Instead of strengthening authoritarianism, US companies and governmental agencies are focused on the commercial applications of artificial intelligence. Simply put, America would rather have advanced self-driving vehicles than an AI-powered version of Orwell's Big Brother. And Biden's national AI strategy will most likely become a counterpoint to how China believes the technology should be used. Several steps have already been taken in this direction. The US National Security Commission on Artificial Intelligence (NSCAI), an independent organization established in 2018 to spur artificial intelligence development in the country, urges the government to form strategic partnerships with private companies and fellow democracies. In addition, NSCAI believes the new administration needs to allot $40 billion to further expand and democratize R&D activities in the AI field. The US government to establish a Technology Competitiveness Council to create national strategies for seven emerging technologies, including artificial intelligence. In particular, US officials are interested in leveraging AI to prevent the malicious use of biotechnology, including genomic weapons. In light of Biden's decision to into the origins of COVID-19 — some theories suggest that the virus was created in a laboratory and released through an accidental leak — the country's priorities do not come as a surprise. AI survey conducted by RELX is also looking order a new investigation The US national AI strategy pivots on fostering technology talent Another issue to be addressed by the national artificial intelligence strategy is, undoubtedly, the aggravating shortage of AI talent. Following a temporary slump in 2020, the demand for AI/ML engineers, and artificial intelligence consultants is again rising. , 39% of companies cite a lack of technical expertise as the primary reason for not using artificial intelligence, while those who successfully adopted AI are seeing "the most acute shortages of talent." Additionally, limited knowledge of artificial intelligence and the underlying technologies 53% of enterprises from deploying AI solutions company-wide. Given that AI skills shortage is a daunting problem in most developed countries, the US government will need to build an entirely new artificial intelligence talent pipeline by improving STEM education and hiring highly skilled immigrants. Private companies are gearing up for the AI revolution too. According to the RELX study cited above, 75% of enterprises now offer some kind of AI training (up from 46% five years ago). Greater availability of artificial intelligence education programs will ultimately help prepare the workforce for AI system integration across all industries and economic sectors. skilled data scientists, According to ZDNet prevents The national AI strategy depends on semiconductor manufacturers Artificial intelligence systems — especially smart applications running on devices rather than in the cloud — require powerful chips. Unfortunately, for decades, the lion's share of computer chips have been manufactured in Taiwan and mainland China, which led to a severe semiconductor shortage in 2020. And we're dealing with its consequences to that day. What initially appeared to be a mere delay in supplies as chip production came to a halt amid the pandemic evolved into a massive crisis, in some way. In particular, the crisis . At the same time, consumer electronics juggernauts like Apple and Samsung the launches of new products or failed to fulfill contractual agreements.As part of the national artificial intelligence strategy, the government is looking to and reduce the country's reliance on hardware companies located in Asia. affecting 169 (!) industries cost the automotive industry $110 billion had to postpone bring chip manufacturing back President Biden requested a whopping $442 million budget for strategic manufacturing programs, including $150 million for two newly established manufacturing units whose sole purpose will be producing chips for industrial equipment, consumer electronics, and other devices with AI capabilities. All of this could help the United States mitigate the aftermath of the ongoing chip shortage crisis caused by the growing shift to online work, education, and entertainment. Meanwhile, a report from NSCAI indicates that $35 billion is needed to give the country's microchip industry a second wind Artificial intelligence becomes part of the national security strategy Artificial intelligence has emerged as a double-edged sword for the government and businesses alike. In addition to helping fight cybercrime and improve safety in public places, the technology underlies cyberattacks and disinformation campaigns targeting the US institutions and critical infrastructure facilities, including . Ultimately, that's another reason why the Biden administration is hoping to lure chip manufacturers back. As the United States and China are lingering on the edge of a cold war, it is essential to prevent the worst-case scenario where semiconductor supply chains could be disrupted indefinitely, sending the US governmental and military organizations and businesses across various verticals into a freefall. The US congressmen the new administration to enforce the nation's security mechanisms, laws, and infrastructure, citing recent ransomware and as a primary example of a rising threat to operational technology. As of 2021, the bills regulating the use of artificial intelligence in at least 16 states. water systems and power grids are also pressing adversarial attacks have been introduced How the national AI strategy will benefit America When we talk about artificial intelligence and , we think about productivity, cost optimization, and gaining a competitive advantage over our rivals. Similarly, the government needs a comprehensive AI strategy to strengthen America’s geopolitical position and national welfare.In the not-so-distant future, President Biden's ambitious plan could yield impressive results, by 2030. its applications in business leading to a 37% GDP growth Additionally, the nation-wide adoption of artificial intelligence will favorably impact: With Russia and China 30% of its military power with AI capabilities by 2030, America needs to rethink its defense strategy, seamlessly integrate AI into military systems, and introduce the government regulation of artificial intelligence to ensure its responsible usage. Military capabilities. planning to augment . According to RELX, 63% of companies that actively use artificial intelligence reported the technology's positive impact on their ability to pull through the COVID-19 crisis. When applied on the national scale, AI could help the country ensure uninterrupted production flaws and near-normal operations of critical infrastructure facilities. Economic resilience . From data scientists and AI engineers responsible for creating and deploying AI systems to specialists with industry-specific expertise who will oversee and adjust the performance of intelligent algorithms, the AI revolution might prove a viable solution to the ongoing unemployment crisis, than it's going to kill. Employment creating more jobs The US society might not fully understand AI's impact on the country's economy, security, and welfare in the coming years — and what the government's plans regarding artificial intelligence really are. To fill in the knowledge gaps, the White House Office of Science and Technology Policy the new version of AI.gov. This website provides a wealth of information about the AI-related laws, strategic projects, and steps the federal government is taking to secure America's dominance in the artificial intelligence field. has recently launched If your company is having a hard time developing your own AI strategy, please ! Together, you'll build a smarter, safer, and better future! contact ITRex team

An In-Depth Look at Biden's National AI Strategy

What the Hell Is the Mar-a-Lago Accord and Why Should You Care?

Šta je, dođavola, Mar-a-Lago sporazum i zašto bi te bilo briga?

¿Qué diablos es el Acuerdo de Mar-a-Lago y por qué debería importarte?

Zer demontre da Mar-a-Lago akordioa eta zergatik axola zaizu?

Qu'est-ce que l'accord de Mar-a-Lago et pourquoi devriez-vous vous en soucier ?

आखिर मार-ए-लागो समझौता क्या है और आपको इसकी परवाह क्यों करनी चाहिए?

Što je, dovraga, sporazum Mar-a-Lago i zašto bi vas bilo briga?

マール・アー・ラーゴ協定とは一体何なのか、そしてなぜ気にする必要があるのか？

Czym do cholery jest porozumienie z Mar-a-Lago i dlaczego powinno cię ono obchodzić?

د مار-ا-لاګو تړون څه شی دی او ولې باید پاملرنه وکړئ؟

Что, черт возьми, такое Соглашение Мар-а-Лаго и почему оно должно вас волновать?

මාර්-අ-ලාගෝ ගිවිසුම යනු කුමක්ද සහ ඔබ ඒ ගැන සැලකිලිමත් විය යුත්තේ ඇයි?

Vad i helvete är Mar-a-Lago-avtalet och varför ska du bry dig?

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