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

Get the most recent info and news about ON Semiconductor on HackerNoon, where 10k+ technologists publish stories for 4M+ monthly readers.

ON Semiconductor Corporation, together with its subsidiaries, manufactures and sells semiconductor components for various electronic devices worldwide. The company operates in three segments: Power Solutions Group (PSG), Advanced Solutions Group (ASG), and Intelligent Sensing Group (ISG). The PSG segment offers analog, discrete, module, and integrated semiconductor products for various applications, such as power switching and conversion, signal conditioning, circuit protection, signal amplification, and voltage regulation functions. The ASG segment designs and develops analog, mixed-signal, logic, Wi-Fi, and application specific integrated circuits and standard products, as well as power solutions for various end-users in the automotive, consumer, computing, industrial, communications, medical, and aerospace/defense markets. It also provides trusted foundry and design services for government customers; and manufacturing services. The ISG segment offers complementary metal oxide semiconductors and charge-coupled device image sensors, proximity sensors, image signal processors, single photon detectors, and actuator drivers for autofocus and image stabilization for a range of customers in automotive, industrial, consumer, wireless, medical, and aerospace/defense markets. The company serves original equipment manufacturers, distributors, and electronic manufacturing service providers. ON Semiconductor Corporation was incorporated in 1992 and is headquartered in Phoenix, Arizona.

ON Semiconductor

Cybersecurity stocks enjoyed a strong 2023 off the back of a generative AI boom that has posed new challenges to the industry as a whole. As rapidly advancing artificial intelligence brings fresh problems and solutions for online privacy and safety, could we see cybersecurity stocks emulate the astonishing success of semiconductor firms like Nvidia over the year ahead? Today, online businesses are having to counter increasingly sophisticated threats. that global cyberattacks have grown between 40% to 45% in 2023, with major breaches striking Las Vegas hotel operators MGM Resorts and Caesars Entertainment as well as genomics and biotech firm 23andMe. Forbes data suggests The , buoyed by advancing technologies like AI, has prompted a widespread increase in cybersecurity spending among businesses. According to Gartner data, worldwide end-user spending on security and risk management is set to in 2024, representing a 14.3% increase over last year. severity of cyberattacks reach $215 billion This heightened emphasis on cybersecurity spending has led some Wall Street analysts to suggest that the industry is next in line for a growth spurt, with generative AI growth helping to pave the way for more sophisticated defensive solutions for leading players. “Cybersecurity is becoming increasingly important as enterprises and organizations rely more on technology,” , head of investment advice at Freedom Finance Europe. “The rise in cybercrime has led to an increased demand for cybersecurity products and services, and it is expected that this demand will continue to grow in 2024.” explains Maxim Manturov In 2023, the emergence of generative AI, sparked by OpenAI’s launch of ChatGPT, paved the way for a significant market rally that saw semiconductor stocks undergo major market rallies, with Nvidia [NASDAQ: NVDA] stock returning more than 220% over the calendar year. Could the growing need for security solutions push cybersecurity stocks into similar territory over the year ahead? Emulating the 2023 Semiconductor Boom The underlying reason why semiconductor stocks like Nvidia and AMD [NASDAQ: AMD] were so popular in 2023 was that they were quickly identified as the firms best placed to facilitate the widespread adoption of generative AI solutions. Semiconductor manufacturers had the power to produce chips to support Large Language Models for businesses seeking to embrace GenAI, and investors decided to bet big on the revenues that this would bring. As we can see from the iShares Semiconductor ETF [NASDAQ: SOXX], the industry as a whole enjoyed significant growth in 2023 that nearly doubled that of the S&P 500 over the year. With both Nvidia and AMD posting triple-digit gains, it’s clear that investor appetite for generative AI was focused largely on semiconductors. Using the First Trust NASDAQ Cybersecurity ETF [NASDAQ: CIBR] as an example, we can see that the cybersecurity industry enjoyed strong growth that also outperformed the S&P 500. But could there be room for online security to take center stage in 2024? Security Risks and Opportunities of GenAI Forecasts suggest that generative AI will become omnipresent across enterprises in the future. As much as have reported that they are or will soon be using Large Language Models (LLMs) as part of their operations, with 57% expressing the same intentions. 69% of public sector companies While generative AI adoption is widely regarded as a powerful disruptive force across a vast range of industries, there are also many concerns surrounding the security threats they could pose. The adoption of LLMs raises concerns about the risks of using third-party models and their capabilities in a largely unregulated space. This rapid growth of generative AI is pushing more organizations into ramping up their artificial intelligence initiatives without sufficient guidelines or regulations in place to ensure that both AI and LLMs are safe and ethically sound. While this means that embracing AI at scale could be seen as a threat, it also offers fresh ways for companies to combat cybercrime. With many small businesses and startups incapable of building security teams to counter threats, defensive AI can offer all organizations a to keep cybercriminals at bay. valuable framework “As AI crosses a new threshold, security predictions at scale are becoming more tangible,” , CTO at IBM Security. “Although early security use cases of generative AI focus on the front end, improving security analysts’ productivity, I don’t think we’re far from seeing generative AI deliver a transformative impact on the back end to completely reimagine threat detection and response into threat prediction and protection. suggests Sridhar Muppidi “The technology is there, and the innovations have matured. The cybersecurity industry will soon reach a historic milestone, achieving prediction at scale.” Innovative Cybersecurity to Bring Wall Street Growth As generative AI presents more sophisticated risks to online businesses, cybersecurity firms that embrace artificial intelligence can be best placed to combat threats more effectively and consistently. This will invariably present new opportunities to investors on Wall Street, with leading cybersecurity stocks already working on comprehensively embracing AI solutions. With this in mind, three cybersecurity stocks could be well placed to emulate the success of leading semiconductor firms on Wall Street should investors turn their attention to AI defences in 2024: 1. Fortinet Inc [FTNT] \Fortinet is no stranger to parabolic growth on Wall Street, having made spectacular gains in the wake of the pandemic-driven push towards digital transformation in recent years. The firm has seen much of its growth emerge from the firewall market, which analysts believe has been steadily improving over the second half of 2023. The beauty of Fortinet is that , managing director at Bank of America, believe that the stock is trading at a “significant discount” in comparison to the firm’s bull and bear scenarios, which still implies a 4% upside in a worst-case scenario. analysts led by Tal Liani 2. CrowdStrike [NASDAQ: CRWD] CrowdStrike became one of the most impressive cybersecurity performers in 2023 and is readily embracing generative AI solutions to add further strength in the future. The firm is a leading resource for endpoint security and specializes in antivirus, detection, response, and combatting malware. In May 2023, CrowdStrike , a generative AI security analyst that provides automated detection and response to assist users of all skill levels to identify and stop breaches in a more simplified manner. The LLM will also enable customers to ask questions in dozens of languages to receive more accurate and intuitive responses from the CrowdStrike Falcon platform. introduced Charlotte AI 3. Microsoft [NASDAQ: MSFT] Tech giant Microsoft always has the power to enter industries and become a market leader, and the recent in-roads the firm has been making in cybersecurity could help its stock to become recognized as a prominent force in the sector. Having recently implemented a new AI assistant within the Microsoft Security Copilot platform, Microsoft’s cybersecurity capabilities are continuing to grow thanks to artificial intelligence solutions. In November 2023, what the company termed “the next major step in this industry-defining vision: combining the power of leading solutions in security information and event management (SIEM), extended detection and response (XDR), and generative AI for security into the first unified security operations platform.” Microsoft announced This AI-driven security upgrade for Microsoft Security Copilot further underlines the stock’s position as a key innovator in the field of . cybersecurity Can Cybersecurity Emulate the Semiconductor Boom? The semiconductor industry benefitted from having a strong relationship with generative AI due to its key role in implementing the technology. While the GenAI boom was a rare instance that brought floods of investor optimism and growth to global markets, it’s reasonable to expect cybersecurity to experience growth as the implications of the technology place a greater emphasis on security. Although emulating Nvidia’s significant 2023 rally may prove to be a little on the ambitious side, generative AI will likely ensure that cybersecurity innovators remain more relevant than ever in the future.

Could Cybersecurity Stocks Be Next In-Line to Emulate the Success of 2023’s Semiconductor Boom?

সাইবারসিকিউরিটি স্টকগুলি কি 2023 এর সেমিকন্ডাক্টর বুমের সাফল্যকে অনুকরণ করতে পরবর্তী ইন-লাইন হতে পারে?

Könnten Cybersicherheitsaktien als nächstes an den Erfolg des Halbleiterbooms im Jahr 2023 anknüpfen?

¿Podrían las acciones de ciberseguridad ser las próximas en emular el éxito del auge de los semiconductores de 2023?

Les actions de cybersécurité pourraient-elles être les prochaines à imiter le succès du boom des semi-conducteurs de 2023 ?

क्या 2023 के सेमीकंडक्टर बूम की सफलता का अनुकरण करने के लिए साइबर सुरक्षा स्टॉक अगली कतार में हो सकते हैं?

2023 年の半導体ブームの成功を真似るのはサイバーセキュリティ銘柄になる可能性があるでしょうか?

사이버 보안 주식이 2023년 반도체 붐의 성공을 모방할 수 있을까요?

As ações de segurança cibernética poderiam ser as próximas a emular o sucesso do boom de semicondutores de 2023?

Могут ли акции кибербезопасности стать следующими на очереди, чтобы повторить успех полупроводникового бума 2023 года?

Siber Güvenlik Hisseleri 2023'teki Yarı İletken Patlamasının Başarısını Taklit Edecek Sırada Olabilir mi?

Cổ phiếu an ninh mạng có thể trở thành cổ phiếu tiếp theo mô phỏng thành công của sự bùng nổ chất bán dẫn năm 2023 không?

网络安全股票能否效仿 2023 年半导体繁荣的成功？

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

In 2021, the global economy was recovering and adapting to the new reality. Some directions, like air carriers, were coming back to the game very slowly; technology companies, on the other hand, showed high growth rates. The companies from the Nasdaq 100 list, FAANG (Facebook, Amazon, Apple, Netflix, Google), vaccine developers, IT, and e-commerce are still on the fast track. On the one hand, the return to the usual way of life depends on them, on the other, the transition to a new stage of economic evolution. Next year they will continue to give, maybe not the highest, but stable dividends. However, a few more interesting points for investment have been added. I would like to break them down for you. 1. Metal trends Although gold remains the king of metals, I will tell you about those that have raised a lot of demand in recent years. These are lithium, aluminum, and magnesium. Lithium carbonate is the cheapest and easiest source for the production of Li-ion batteries. The prices of lithium compounds are breaking records in the entire history and continue to rise. Manufacturers of batteries for machinery and vehicles require more and more raw materials. This year, the global industry has of lithium carbonate. According to forecasts, by 2030 this figure will grow to 1.8 million tons. The extraction of lithium is a complex and time-consuming process. This explains its deficit and the rise in prices for its compounds. Recently, analysts at the consulting company Evercore the beginning of the "lithium decade" and advised investors to invest in this metal. used 430,000 tons announced Prices for aluminum and magnesium skyrocketed as well. Most of all — because of the situation in China. The production of both metals requires huge electricity costs, and after the flooding in Shaanxi province, the energy crisis in the country. On the other hand, before the 2022 Winter Olympics, the Celestial Empire continues to struggle with harmful emissions and set quotas for industry, which limits production. worsened Therefore, aluminum showed a 13-year high, from $2000 per ton in January to $3160 in October. Wood Mackenzie consulting company that the situation in China has led to a 7% reduction in annual aluminum production. jumping reports Metal imports to China are , as is the demand of the world economy. Statista that the aluminum market will grow 2.6% per year and will reach 78.4 million tonnes by 2029. And Goldman Sachs that by 2023 the price of aluminum will reach $3250 per ton, and by 2025 — up to $3500. growing predicts claims Magnesium deficiency in the world, is also produced by China, is leading to a severe crisis in the automotive industry. Magnesium is required for the production of light and ultra-light alloys. 87% of which Until recently, of the country's magnesium was smelted in Shaanxi. This September, 35 of the 50 factories in the province shut down. It is no surprise that the metal jumped in price from $2,000 per ton at the beginning of the year to $10,000 in September. Due to that, the European Automobile Manufacturers Association has even . 60% warned against catastrophic impact The situation seems to be stable for now. Chinese factories resumed production in October and the price of magnesium to $5,500 per ton. Warren Buffett would recommend investing in this sector immediately. Moreover, according to the CRU Group , due to the dominance of China in the segment, magnesium deficiency will remain. dropped forecast 2. Semiconductors Last year, the capitalization of the semiconductor segment amounted to $426 billion, and by 2028 it will to $803 billion. The main growth factor is the soaring demand for microchips based on semiconductors. And so far it can not be completely satisfied: either the automotive sector or the production of gaming and household appliances complain about disruptions in supplies and production. increase Almost everything related to semiconductors is profitable. The exception is Intel, which temporarily dropped out of the microprocessor race after a change of leadership and got stuck in 10nm technology. In addition, since 2020, Apple has been transferring its desktops and laptops from Intel chips to its own developments. AMD, for example, is already working hard on 7nm technology. AMD shares started from $92 at the beginning of the year and rose to $157 by the end. Tesla cars and Sony PlayStation consoles are equipped with AMD chips. They are also used for Microsoft Azure, Google Cloud, and Meta Oculus services. But Intel isn't giving up. As the Nikkei Asian Review this summer, by the end of 2022, the company may immediately step into the 3nm era in cooperation with the largest Taiwanese manufacturer TSMC. However, Apple is also starting such cooperation at the same time. reported 3. Small cap companies It’s quite simple: it is more difficult for a large company to grow, and your dividends from long-term investments in large business will be less compared to investments in a promising, but small company. For example, the 2021 edition study says that if you invested in a high-cap company in early 1926 and reinvested all dividends by the end of 2020, you would earn 10% per annum. Each dollar invested back then would turn into $11,000. Stocks, Bonds, Bills, and Inflation But if under the same conditions, you were to invest in a small-cap company, then every dollar would turn into $42,000. By comparison, corporate bonds would yield only $300. Factoring out inflation. Getting back to our reality, remember: at the end of February 2020, the shares of many technology companies went down dramatically, but after a month they began to rise again. For the rest of 2020 and throughout 2021, Nasdaq top positions have grown by leaps and bounds. For example, Alphabet shares have grown from $1,100 to $2,900 since early April 2020. Most investors still continue to invest in large companies, but will soon switch to other indices. The most attractive small caps are monitored by several indices, the main ones being the and . S&P SmallCap 600 Russell 2000 In early November, both indices climbed to record highs and are likely to continue to rise, as the overwhelming demand for shares in large projects has remained neglected. They are still cheap in an overheated market. Zacks Investment Research this sector to grow in the next year as the US economy recovers. predicts 4. India It looks like next year the interest of international investment companies will be riveted on India. Morgan Stanley a strong recovery in the Indian economy and 7% GDP growth over the next four years. predicts Initially, Indian stock markets have outperformed other emerging markets over the past year and a half. Morgan Stanley that after India is included in the global bond indices, the Bombay Stock Exchange will begin to grow even faster. This rate will reach 16% by December 2022. believes Next, while in other jurisdictions, such as the United States and China, the topic of raising corporate taxes is really hot, the Indian government, concerned about GDP growth, is taking measures to stimulate corporate profits. And last but not least, the pandemic had a positive impact on the country's export potential, in particular, on the software outsourcing market. 5. Bitcoin The Collins English Dictionary, published by one of the world's largest companies, HarperCollins Publishers, NFT as 2021 word. named Blockchain is here to stay with us for a long time. Like its most famous project — bitcoin, which is the top cryptocurrency because it is the first, and the rest of the crypto appeared later. Conventionally, you can invest in modern hypercars, but the most expensive car still the 1963 Ferrari 250 GTO worth $70 million this year. remains Despite criticism, the price of bitcoin is constantly growing: having reached a capitalization of $ 1trillion, it is among the , with giants like Apple, Microsoft, Alphabet, Saudi Aramco, Amazon, and Tesla. In 2021, its rate increased by 50%. top 7 most expensive projects Of course, Bitcoin has problems. Firstly, it is not the most advanced protocol in terms of code. Secondly, Bitcoin still exists, parallel to the modern financial system, and this situation is changing very slowly. Third, it remains a high-risk asset. However, top crypto assets are gradually taking over the role of an alternative store of value. Among the owners of Bitcoin are Tesla founder Elon Musk, PayPal founder Peter Thiel, investment bank Morgan Stanley, TIME magazine. My advice is still the same, that is - to invest as much as you don't mind losing. Do not engage in synchronized jumps with sharp market fluctuations, but be ready to play long and feel periodic anxiety of your nervous system.

Investment Areas for 2022: Lithium, Small Businesses, India, and More

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

Conflicts, disruptions, and backlogs – supply chain logistics management has become a battlefield. And now, with new demand streams and the crisis between Russia and Ukraine, resources limited, access routes denied, and workers replaced to support their homes in crisis regions, Europe is facing a whole Pandora’s box of logistical issues. Russia and Ukraine supply , 48% oil, and 40% gas to European countries. With sanctions on Russia, and Ukraine's resources up in smoke, exporting the available supplies is reduced to impossible, not to mention production preparation for the following year. 30% wheat Countries are juggling shifts between freight methods, routes, and suppliers with closures such as which would take goods from China to Eastern and Western EU. Now that airlines need to bypass Russia, this expensive alternative still provides complications. the Russian railway, The auto industry's largest German brands depend on semiconductors that pass through Russia. Big players are already beginning to localize the production of missing parts from their EU suppliers. Tesla opened two new plants: in Berlin, Germany, and its hometown Austin, Texas, as a strategy to improve the cost and efficiency of distributing vehicles from factories to customers in major European markets. Many companies are looking to move their production centers locally to stabilize their operations. While reshoring may be the path to a sustainable future, not all countries have the resources, funds, or infrastructure to bring production home in response to a conflict. And for those that do, rebuilding is a timely process. Amongst a crisis and already limited supplies to the norm, what can European businesses (large and small) do to tighten their logistics and future-proof operations? 'Democratizing' the Power That Bigger Players Already Have The biggest challenges always land on the most vulnerable players. This crisis is less about debt and more about feeding hunger. With the World Bank predicting an almost due to the conflict and scarce grain supplies, these record rises would push hundreds of millions of people into poverty and lower nutrition. 37% jump in food prices Retail giants who operate nearly all of their supply chain in-house can absorb rising costs of fuel, utility prices, and subsequent product value increases in their cash cow business model. Yet, smaller companies become powerless as providers and shipping companies choose to operate with those who pay. In response to immediate needs, the EU has funded a total of €143 million in humanitarian aid to support the people affected by Russia's war on Ukraine. Nonetheless, payouts and grants are not sustainable long-term. Ongoing public innovation programs like can help smaller companies strengthen their niche and offer innovative solutions to a bigger spectrum of clients. With a budget of €95.5 billion, their goals are to boost economic growth and strengthen the EU's competitiveness – providing participating companies meet specific criteria and implement EU policies. Horizon Europe The problem is when smaller companies drown in regulations and contracts, grants or program applications can form a lengthy, challenging process. As we saw with – which rejected a government payout during the pandemic when realizing others were in more need – those who get accepted more often have the time and resources to hire someone who completes the process while maintaining business as usual. Shake Shack So, what are the most significant difficulties for logistics providers? And if grants are simply a short-term solution, what can the smaller earners do to keep up long-term? Three Key Pain Points and Solutions for Logistics Providers In the face of the dynamic situation, resources are limited, costs of goods are increasing, and the EU is facing continuous . Three trucker unions opted to join a week-long strike in the face of rising fuel prices. After deciding the €500 million government support package didn’t suffice – the truckers' absence led to further food shortages across the continent, and the cycle of rising costs continues. trucker shortages In addition, the Black Sea is off-limits, along with the Azov Sea, Russian throughways, and the China train. Shipping companies purchase aircraft, airports alter routes, and destinations change daily. Companies like combine end-to-end transport solutions for all types of goods via shipping, overland, and airfreight services – the trend is to provide agile transportation by increasing control centrally. But data varies from the number of decimal points to the metrics used to scale. When the information from suppliers reflects different formats, central control gets complex. CMA CGM In a crisis with minimal supplies, delayed routes, and shared control, businesses large and small are left with little choice but to streamline their operations and let technology absorb some of the work. 1. Strategic Capacity Management To combat a smaller workforce, ensuring each container, vehicle, and ship is fully loaded will reduce the number of journeys – and drivers – required to meet demand. The number of empty vehicles on road totalled in 2020 due to last-minute needs and the use of historical schedules. 20% of freight transport Artificial intelligence (AI) that assesses each business’s assets, and capacity, can ensure each resource used is at full to best meet priority demand. With new sanctions and blockages made inconsistently, central teams must know in real-time what orders to expect to make immediate amendments to the fleet. They request a larger trucker to fulfil the delivery or fill the vehicle with predicted upcoming demand. In February 2022, of the container fleet capacity was unutilized, primarily due to the impact of the COVID-19 pandemic on the global supply chain. In states of panic, logistics providers were eager to meet volatile consumer requests, and existing plans became obsolete which created port congestions and vessel delays. For example, the Los Angeles port saw clogging bays, while a lack of organization between businesses left roadways blocked for truckers to drive out with their goods. 11.6% empty containers Now, with missiles flying in the European skies, fears of crew safety increasing insurance premiums, and cancelled ships rising value of goods, logistics providers must maximize the use of every vehicle, cargo, and air freight departing. Capacity management within each vessel, container, port, and bay will support more efficient storage and minimize the number of assets required per delivery – freeing up additional space for clearer throughways. By feeding the intelligent technology with data in real-time, companies can reduce the room for error and ensure every last-minute change is incorporated. 2. Route Optimization In the current situation, with dangerous and unpassable parts of the Black Sea and Sea of Azov and shut, shipping companies face serious complications in route planning. Logistics providers already must design routes based on many factors – demand, destinations, road conditions, weather, port congestion, ships available, drivers on duty, and their hours of work to maintain a healthy workforce. Yet, they also have to factor in new lane closures and avoidance of war zones. airspace over Ukraine is constructing the most efficient route between transportation modes, cities, and even countries. Businesses can improve their unique supply chains with AI to consider vast amounts of data and produce the most efficient scenarios. Linehaul planning Imagine you are strapped with six drivers, multiple vehicles available, and volumes required in eight destinations. By breaking down eight locations into six trips with the most convenient drop-off points, logistics providers can calculate what vehicle size they need for each journey to meet the demand. AI that reads data and weighs countless scenarios in seconds can run any new route closures, unsafe zones, and updates to provide the most efficient routes. On top of this, AI can predict each journey length based on real-time traffic data and miles to ensure fair distribution of workers following health regulations, hours worked, and sleep requirements. In addition, including maintenance needs means logistics providers can generate optimal schedules that factor in wear and tear to minimize breakdowns. Let’s say your truck is driving the E6 (from Oslo up to Kirkenes). AI that reads the journey length, number of maintenance stops on the route, and vehicle engine levels centrally can direct drivers to the nearest garage to make minor repairs before further damage or breakdown between service stations. Maximizing resources, speed of passage, and minimizing costs are crucial when facing a crisis. With optimal routes and fully functioning vehicles strategically loaded for efficient drop-offs along the way, businesses can utilize every asset and driver time at full capacity. However, what happens when companies don’t open-source their logistics data, and all businesses plan to take the same route? 3. Connecting Companies From Multiple Supply Chains Businesses that streamline their logistics are ahead of the game. But imagine if they could stay in the loop with other companies in the ecosystem too? When all companies are aware of the ships going out, planes in flight, and roads in congestion, they get a whole new leap of future insight into potential traffic jams and routes available to prioritize and plan. There is no more critical time to work together than during a war. Nonetheless, If one system sends data in meters and the other in feet, the time taken to convert information can delay deliveries further. When your data analysts spend cleaning out data so that it talks to other bits of data, only 10% of the time drives the total value out of the information. 90% of their effort The Digital Container Shipping Association (DCSA) transforms the shipping industry by setting across all aspects, from the bill of lading to gateway connectivity interfaces and bay plans. The goal is to enable global, industry-wide interoperability, minimize port congestion, and improve efficiencies in line with sustainable practices. When logistics providers follow these, they can join forces with new suppliers and networks more efficiently. data standards For example, you produce flour in Turkey, have a delivery network, and your usual supply of wheat from Ukraine is unavailable. You have the option to purchase from Ireland, but you need new carriers to transport the goods part of the way. By following the same data standards, you can easily integrate platforms, share quantities, bid drivers, and communicate updates proficiently. And when handling large volumes of data, AI platforms that can read all the required information at once are what will make collaborative processes manageable. The Future: Localization and Collaboration Teaming up with your trusted partners and new suppliers to source items nearer to home, reduce carbon footprint, fuel required, and the subsequent cost savings are clear. Collaboration to protect human rights and prevent environmental abuse requires structured governance. The is drafting a law that requires all companies with over 500 employees and a turnover of €150 million to carry out due diligence across their business operations. While the legislation only applies to less than 0.2% of EU companies, the process will require investigation into entire supply chains – covering the smaller third parties in collaboration. The goal is to bring a united front to prevent human rights and environmental abuses, paying particular attention to agriculture and fashion, where the risk of exploitation is traditionally high. EU Commission For operations to run at optimal efficiency to combat the conflict’s rising fuel costs and large sums of volatile data to manage, integrated platforms with real-time data visibility are essential. Companies with AI systems in place will be prepared with transparent data and can expect smoother, cost-efficient due diligence processes with reduced risk of fines and auditing fees. Cost reduction and increasing the sustainability of supply chains are at the advantage of all businesses. Efficient use of assets, capacity, and workers and optimized routes with strategically planned maintenance and drop-off points drive value. When suppliers and shipping providers start collaborating, they save costs further and can improve their united agility in the face of disruption. Swapping suppliers and moving locally to overcome sanctions is made even easier if companies are working as a connected continent with integrated supply chains. This is the future.

How the Conflict Between Russia and Ukraine is Affecting Supply Chains

The semiconductor industry, despite its innovative core, is traditionally known for its conservative approach, particularly in adopting new manufacturing processes and technologies. This cautiousness stems from the high stakes involved: even minor errors in chip production can lead to significant financial losses and delays in product launches. However, now this sector is undergoing a pivotal transformation. My recent conversation with Lynceus's co-founder, David Meyer, revealed the startup’s innovative strategy: leveraging AI not as a tool, but as a transformative force to optimize processes and amplify output in chip production. How did you come up with the idea of applying AI to semiconductor manufacturing? What was the initial goal and idea behind Lynceus? My co-founder, Guglielmo Montone, spent 15 years as an AI researcher where he was looking at different techniques that enable the training of AI models on very dynamic and unstable data—that is, something that you find a lot in manufacturing. We started the company by applying this to semiconductor manufacturing with the idea to automate all processes from the beginning to the end. This means that you're going to have an algorithm to continuously calculate the optimal parameters to put in the machine in order to obtain the best quality. But initially, this was very hard to sell because you're telling manufacturers that you have a black box to run their machines. We understood that if you're going to change the parameters of a machine, you need to be able to anticipate the specific impact on the quality of the output. And so there is a quality prediction element in the core of our technology. How do you sell your product to manufacturers now? What results can you guarantee? We are showing the specific tangible impact that your tool can have if implemented. And at the same time, you have to show them how this is going to scale because if we're just going to deploy one model somewhere, it's not very interesting. What we're doing is establishing a new way of managing the performance and the productivity of this type of factories, which is based on simultaneous optimization. This includes products’ quality, volume and costs - the three main KPIs of manufacturing. Traditionally, manufacturers can only optimize one of those at a time. Our models also generate data that gives real-time visibility on what is happening inside the machine. Manufacturers don’t have it today, and it can feed a number of other systems in the factory. Maintenance scheduling or automation systems can both benefit from more granular and real-time data on how a process behaves. This sounds like it can also be used outside the semiconductor industry. Do you have plans to scale your business in another direction? Absolutely. Initially, we started Lynceus to optimize the production of vaccines, where you have to grow cells in a bioreactors, and you don't know how many cells you're growing. If there’s not enough, you have to throw everything away, so we have built an algorithm to predict if the cell culture is going to be successful or not. It worked quite well, but then the traction picked up on the semiconductor side of things. Another market could be all the industries similar to semiconductor manufacturing but less sophisticated. LEDs, building the screens for the iPad, TVs, etc. Solar panels could also be a good one. There's also battery manufacturing, where we would like to expand on in the future, but we haven’t investigated it so far. 30% to 35% of what they produce is not good enough, and there's a lot of complex chemical reactions to make sure that their metal has the right properties, which can be optimized with data. How does your experience in managing transportation companies like Uber and then Circ help you grow Lynceus? Even though I do not come from semiconductor manufacturing, I have experience scaling operations. I know how to go from zero to one, start a project, make people work, structure, set objectives, and make sure that we hit them. And I think not coming from semiconductor manufacturing is actually something that helped me in this venture because it's a closed and conservative world, and I wanted to disrupt it. When we started, everyone told us that what we wanted to do was impossible. And so if we listened to people who've been in the industry for 20 years, we wouldn't even have tried and started what we did. I think arriving with another perspective and just continuously trying, helped us to get to where we are. What are some significant milestones you've achieved so far? I think the main achievement we have is that we deployed the first AI model in production in our customer’s fabs, and broke into the semiconductor industry, which is notoriously conservative, in three years. I think the challenge here is just the sheer kind of ramp-up that you need to have in order to be credible in any discussion with PhDs who work on the same machine for 20 years. You have raised $10M for Lynceus. What would you define as your major drivers to success? Was it hard to talk to investors given that you're working in a complex industry? Yeah, it is hard to find the right investors. We work with big semiconductor manufacturers, so we end up having discussions with a lot of investors. But not many of them eventually have an appetite for this type of product. When we started raising, it was the beginning of COVID. We talked to around 85 investors and ended up with three offers. The first thing for us is to make them understand what we do in a good level of detail. Investors see companies every day so they tend to have these kinds of boxes like this predictive maintenance or this is AI for manufacturing - to see whether we fit there or not. But we need to get deeper and explain exactly what we do and the ROI of our solution. What's the difference in the fundraising process right now given the AI hype? COVID was hard, especially because it was the first time I was doing a fundraising myself. So it was much harder to build any kind of connection with investors at this time. And I think it's an important part of the journey. We have investors who liked us and ended up working with us. The appetite for risk is now lower than before. Now it's all about these questions: How much revenue do you make, are you profitable, can you guarantee that the market is huge and there's not going to be any legal risk and competition? You need to understand which firms are interested and make sure that you screen the time to  spend with each of them. You mentioned the competition. What does the landscape look like in the AI industry for semiconductors? The good thing about manufacturing is that you have an infinite amount of use cases once you contract a big factory. We're starting to see some competitors emerging, but those startups are all spin-offs from big players existing in the industry. So you might see a company in which all of the execs come from a major chip manufacturing company, and this company invested in their series A. Or there’s another one that is half acquired by a major manufacturer of machines for those factories. I think being independent is our great advantage. How do you see the current landscape of AI in the manufacturing sector evolving, and what trends do you find the most impactful? As we started having discussions with 15-20 major semiconductor manufacturers, the first takeaway is that everyone takes our business seriously. We haven’t seen a single major company that has never tried to implement AI in some form so far. We are also reaching maturity in terms of the data infrastructure that would support the deployment of this kind of solution. This required a lot of investment over a long period of time. There’s finally something that produces payback for this investment now. The third one is that we're seeing companies trying to do this themselves and then coming back from this decision because they don't want to do it on their own anymore. That's how we gained one of our first customers. This solution can be applied across different factories, machines, and teams in the same factory that needs to be implemented and maintained in very close collaboration with IT and their quality team, because every new tool or new production process you introduce needs to be vetted there. To execute this, you need to have a transparent and efficient communication and sharing of information between those teams who work a little bit in silos. Companies that try to do this on their own often have difficulty breaking those silos, but we can provide a solution for that. What do you think are the most useful qualities for an entrepreneur in this space? What personal skills do you believe helped you to grow that fast and to that scale? The first one is to be stubborn and tenacious. We are talking about people who have a day job, where they need to run a factory with 3,000 people. So if they get on a call about something they're going to be skeptical of, you have to keep trying and also be open to understand what works and what doesn’t and adjust your message based on that. Another one is having an ability to structure things quickly, even though you're working in a very unstructured environment. So maybe you just developed one part of your product and you don't exactly know how it works, but you need to be able to go in front of a customer and present a structured deployment process and say how it’s going to work and what the process is. The last one is most relevant to working at Lynceus. Whether in consulting, or at Uber and Circ, I worked with like-minded people - very operational with the “let's go and tackle the project” approach. Here, working at Lynceus, I need a different set of skills. My co-founder is an AI researcher who's worked in a lab his whole life on a different type of scale and timelines and everything. And so it's crucial to be able to open up and listen to people and accept other ways of working, but at the same time be conscious of the strengths and the skills that come from your experience. It's keeping the best of what you learned while being able to adapt it and make sure it fits with the other types of skills that you need.

The Future of Chip Making: Using AI to Minimize Testing and Maximize Throughput

Photo by Rafael Pol on Unsplash In the ever-evolving computing landscape, one concept has become not just a trend but a necessity: parallel programming. The days of exponentially increasing clock speeds driving improvements in single-core performance, as predicted by Moore’s Law, are behind us. Instead, the future of computing lies in the realm of parallelism. Moore’s Law is falling behind :( Picture this: colossal amounts of data pouring in from every corner of our digital world, complex simulations demanding more computational power than ever, and artificial intelligence algorithms hungry for processing capabilities. In the face of these challenges, the single-core processor is no longer the hero of the story. To meet the demands of modern computing, we need to harness the power of parallel programming. In this series of articles, we embark on a journey through the world of parallel programming, exploring its profound significance and practical applications. In this inaugural installment, we’ll lay the foundation by addressing two fundamental questions: and Why is parallelism a necessity, what are the tangible benefits it brings to the table? As we delve into the heart of parallel programming, you’ll discover that it’s not just a choice for optimizing software performance; it’s a requirement in today’s digital landscape. By the end of this article, you’ll gain a deeper understanding of why parallelism is indispensable and how it revolutionizes the way we approach computing challenges. So, fasten your seatbelts as we embark on a journey to uncover the transformative power of parallel programming. A brief history of parallelism Parallel computing, with its roots dating back to the early days of computing, has a storied history. In the early 1970s, the was a pivotal milestone when it was delivered to NASA Ames Research Center. Despite its conceptualization beginning in 1952, construction only commenced around 1966. The remarkable Illiac-IV featured , each functioning as an autonomous mini-computer. It delivered remarkable computational prowess, achieving up to , a groundbreaking record for its time. Illiac-IV a single core housing 64 processing elements in a 4x4x4 cube formation 200 MFLOP/s : 200 MFLOP/s Illiac -IV in 1972 Apple iPhone 8 in 2017: 297 MFLOP/s , a groundbreaking advancement in the realm of high-performance computing, took center stage with the introduction of the iconic supercomputer. While the Illiac-IV excelled in parallelism, the Cray-1 harnessed the transformative potential of vector processing. Conceived by the visionary Seymour Cray in the mid-1970s, this pioneering machine harnessed specialized hardware to execute operations on entire arrays of data within a single instruction. In addition to its 8 general-purpose registers and 8 floating-point registers, the Cray-1 featured , each boasting a 64-bit width, unlocking unprecedented computational power. Vector processing Cray-1 8 vector registers The Cray-1 achieved peak performance, reaching an astounding , setting a new standard for high-performance computing. Cray Research’s subsequent models continued to push the boundaries, with companies like Hitachi and Fujitsu following suit in their own product offerings. Remarkably, vector processing remains as relevant today as it was in its inception, serving as a foundation for modern computing architectures. 240 MFLOP/s Since then, a continuous stream of innovations has transformed the landscape of computing. From the inception of , which interconnected multiple PCs to amplify computational power, to the integration of external accelerators such as GPUs (Graphics Processing Units) and FPGAs (Field-Programmable Gate Arrays), parallel computing has undergone a profound and ongoing evolution. Cluster Computing The TOP500 list is a well-known ranking of the world’s most powerful supercomputers. It is published twice a year, typically in June and November, by a group of international experts in high-performance computing. The list ranks supercomputers based on their performance in a standardized benchmarking test known as the High-Performance Linpack (HPL) benchmark. The Need for Parallelism Moore’s Law and the End of Single-Core Performance Gone are those days when upgrading your processor would speed up your application by 3x. Better or for worse, the single thread performance is saturating, and people keep boasting that ‘ . So, what exactly is Moore’s law? Moore’s Law is Dead!!!’ Moore, who co-founded Intel Corporation, noted that the number of transistors on a semiconductor chip doubled approximately every two years, leading to a consistent increase in computing power while reducing the cost per transistor. Despite being widely called a ‘ ,’ it was an empirical observation and prediction. Essentially, it states that: law Every 18 to 24 months, the number of transistors on a microchip will double, while the cost per transistor will halve. Moore’s Law had profound implications for the technology industry, as it drove the development of increasingly powerful and smaller electronic devices. It became a guiding principle for the semiconductor industry and a source of inspiration for innovation and investment. For decades, semiconductor companies focused on increasing the number of transistors in their microprocessors and the increase in performance followed almost in parallel. However, several factors contribute to the perception that Moore’s Law is ending or at least slowing down. A pivotal turning point occurred around 2005 when it became evident that the linear correlation between the increase in transistor count and single-threaded performance was no longer as pronounced. Simultaneously, CPU clock frequencies had already approached saturation levels, further underscoring the challenges in sustaining traditional performance scaling. So, why is this happening? With the limited space vendors have on their microprocessors, they can only fit a certain number of transistors. Until now, with advancements in nanotechnology and related manufacturing regimes, it was possible to shrink the size of transistors, thereby resulting in more transistor density on the exact size of the board. However, as transistors shrink, they approach the fundamental limits of atomic and quantum physics. When transistors become too small, quantum effects such as tunneling start to dominate, making it challenging to maintain reliable and predictable behavior. Similarly, further miniaturization of transistors requires increasingly complex manufacturing processes and materials. These challenges not only increase costs but also limit the rate of progress. In addition to the economic constraints and feasibility of manufacturing processes, heat dissipation becomes a significant issue. High power densities lead to overheating and reduce chips’ reliability, lifespan, and performance. One perspective on why many people believe that Moore’s law is nearing its end is that the doubling of transistor density no longer means that the cost is getting halved. While this may be a matter of one’s perspective, what truly matters here is that alternatives must be found, and progress made. Data-Intensive and Computationally Intensive Applications Modern applications, including data analytics, AI, and simulations, crave immense computing power. , for example, must swiftly process colossal datasets. This involves sorting, filtering, aggregating, and generating insights from vast information. Parallelism splits these tasks into smaller, concurrent chunks, hastening analysis and enabling rapid data-driven decisions. Data analytics Similarly, , particularly deep learning neural networks, is computationally intensive. It involves processing large datasets and adjusting model parameters through numerous iterations to optimize performance. Parallelism expedites model convergence by distributing computation across multiple cores or GPUs, allowing larger, precise models to be trained within feasible timeframes. training AI models like weather forecasting and fluid dynamics involve intricate numerical calculations. These simulations often involve solving partial differential equations and running simulations over extended periods. Parallelism divides simulations into smaller solvable segments, slashing time and facilitating high-resolution modeling, thus advancing scientific research. Scientific and engineering simulations Big Data and Scalability In recent years, there has been an unprecedented explosion of data generated from various sources, including social media, IoT devices, sensors, e-commerce, scientific research, and more. For instance, social media platforms generate billions of posts, images, and videos daily, while IoT devices continuously collect data on environmental conditions and machine performance. Genomic research and e-commerce platforms also contribute to this data tsunami. This deluge of data is commonly referred to as “big data,” characterized by its immense volume, high velocity, and diverse variety of formats. How do we handle this insane amount of data? Scalability is the capability of a system to handle increasing workloads and accommodate the ever-expanding datasets without compromising performance. In the context of big data, scalability is vital for several reasons. It allows organizations to manage growing data volumes effectively by horizontally scaling and adding more computing nodes to handle larger datasets without performance degradation. Scalable systems also ensure rapid processing to support real-time analytics and decision-making. Furthermore, they offer cost-efficiency by enabling the allocation of resources as needed, reducing upfront hardware costs, and optimizing resource utilization. The combination of parallelism and scalability is essential to tackle the challenges posed by the influx of big data, empowering organizations to extract valuable insights and drive innovation. This brings us to the end of this article. In this post, we discussed briefly how parallelism emerged as a natural choice for extracting maximum performance and how its relevance will keep increasing. In the upcoming posts, we’ll discuss the fundamentals of parallel programming, specifically talking about the theoretical and programming aspects of parallel applications. Until then, keep parallelizing! Also published . here

Why Parallel Programming is a Game Changer

Automation & the job market — will it happen as fast as we think? Or is it already here? A 1978 documentary looks at the impact of the silicon revolution. The iPhone is ten. The coming tech backlash. Putin’s secrets. Wind power. Have great conversations! ❤️ Love this? (finally the correct link!) Please share it via Twitter 🚀 Forwarded this from a friend? . Sign-up to Exponential View here Dept of the near future 🤖 , according to McKinsey. The firm suggests half of our work activities might be automatable by 2055, so more conservative than the Frey & Osborne forecast. (Full report & .) Job automation will come but not as fast as some fear exec summary here 📺 . Superb 1978 BBC documentary on the semiconductor industry, Silicon Valley’s networked advantage, the rise of IT and the knowledge economy and automation and job losses. Some of today’s issues were hot topics 39 years ago. The chips are down 📱 Horace Dediu: And the most successful product of all time — in fact our first $1,000,000,000,000 product. The iPhone is ten years old. 😱 EV subscriber, Ross Mayfield: (See also: — in contrast to their employee’s personal positions.) The coming tech backlash Silicon Valley companies take a turn to the right 💷 Guy Standing on . “The 20th-century income distribution system has broken down irretrievably. Globalisation, technological change and the move to flexible labour markets has channelled more and more income to rentiers and wages have stagnated.” why we need a universal basic income ☀️ are likely to exceed 1m in 2017 Global sales of electric vehicles Dept of automation and jobs What has really happened to manufacturing? More innovation, higher productivity, greater complexity and a nuanced picture, according to in this excellent report The Economist: Industrial manufacturing was never as simple as those far from the shop floor imagined it to be. Today it has become more complex still. The underlying point is that automation goes hand-in-hand with outsourcing, de-verticalization, specialisation, global supply chains and a generous helping of political pork & its ilk to make up today’s manufacturing smorgasbord. China is . Industrial robot deployment has started to take a familiar exponential pace. Fifty years for the first 1m industrial robots with 8 years for the next 1m according to the . Yet when I look at their forecasts, I just wonder whether they have really accommodated the pretty rapid recent breakthroughs in deep that have so enhanced machine vision, learning, coordination and control systems in the past 2–3 years. My hunch is that deployment will go faster once it really starts.) investing more heavily in robots than any other nation International Federation of Robotics learning Why Trump (and other politicians) don’t talk about automation. than human Robot can operate on eyes more accurately NYC Taxi and Limo drivers . (ht @reillybrennan) lobby for a 50-year ban on autonomous vehicles 🔥🚗 The by 2025, says Ark Invest. market valuation of autonomous transport-as-a-service could be treble that of all automakers Dept of AI Extract of a nice Reddit AMA by Joanna Bryson, a Bath University professor, . on AI, ethics and personhood questions How (from its human curators). AI picks up prejudice Using from selfies machine vision to detect medical conditions hire new candidates, judge suitability for promotion Speech analysis software is helping employers How . “It would be insane to do this by hand.” algorithms helped design the Elbphilharmonie How chips are evolving to accommodate deep learning Exponential Salon: Robots, Up Close and Personal 🌟 Our first event of the year is a dinner salon on the living and loving with robots. Our interlocutor is Dr Kate Devlin, from the Department of Computing at Goldsmiths, University of London, to explore the implications of human-robot relationships as we develop closer connections with AI. What does getting personal and intimate with robots mean for our physical and emotional wellbeing? Why and how does robot intimacy offer new opportunities for self-expression and freedom, or not? The event will be participatory and include dinner and drinks and is kindly supported by BGF Ventures. February 9th, 1830 in central London. , so please . Will be awesome to see you sign up now Short morsels to appear smart at dinner parties 🇷🇺 . (Exceptional read) The secret source of Putin’s evil 👟 Lucy Rycroft-Smith: I wore men’s clothes for a month — and it changed my life 💉 Ambrosia: (in the name of longevity.) a startup allows you to buy young people’s blood and bigger than anyone thought Digital ad fraud is out-of-control Lemonade, a tech-powered insurance agency, resolved and settled a claim for a stolen goose-down parka in 3 seconds. . These things cost $80,000 about 8 years ago. Velodyne’s LIDAR runs to $50 Students take to trans-cranial direct stimulation 🍃 in the UK last year Wind power produced more electricity than coal Reader special I’m helping my buddy John Battelle with his new Shift Forum, a private conference bringing together incumbents, , investors and policy leaders. John’s pulling together a really good confab to discuss many of the issues covered in and how they may play out in business, government and policy. The AI panel, with Shivon Zillis, Vivek Wadhwa and Francesca Rossi looks quite fun :) startups Exponential View The event is invite-only. John and I agreed that EV readers can skip the queue and get . Make sure to use the code Forum17. (The event is in February 6–8.) a $1000 discount by clicking here End note The occasional interesting graphic. We are finally on Instagram. Come and find us. ‘Heart’ something you like. I’ll leave you with this amusing cat video about job-stealing robots. Cheers, Azeem is how hackers start their afternoons. We’re a part of the family. We are now and happy to opportunities. Hacker Noon @AMI accepting submissions discuss advertising &sponsorship To learn more, , , or simply, read our about page like/message us on Facebook tweet/DM @HackerNoon. If you enjoyed this story, we recommend reading our and . Until next time, don’t take the realities of the world for granted! latest tech stories trending tech stories

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