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Samsung is investing $356 billion in chips, biotech and AI

Engadget

Many folks mainly associate Samsung with smartphones and TVs, but the company is looking at other parts of its business for long-term growth potential. In the five years to 2026, it will plow 450 trillion won ($356 billion) into strategic areas, with a focus on things like semiconductors, biotechnology and artificial intelligence. This marks Samsung's largest investment pledge to date and it's an increase from a 240 trillion won commitment it made last August. The figure is 30 percent more than the 330 trillion won the company invested in itself over the previous five-year period. The Samsung Electronics division will use the funding to bolster its chip design and manufacturing process, according to The Korea Herald.


Smart operators: How leading companies use machine intelligence

#artificialintelligence

Making good use of data and analytics will not be done in any single bold move but through multiple coordinated actions. Despite the recent and significant advances in machine intelligence, the full scale of the opportunity is just beginning to unfold. But why are some companies doing better than others? How do companies identify where to get started based on their digital journeys? In this episode of McKinsey Talks Operations, Bruce Lawler, managing director for the Massachusetts Institute of Technology's (MIT) Machine Intelligence for Manufacturing and Operations (MIMO) program, and Vijay D'Silva, senior partner emeritus at McKinsey, speak with McKinsey's Daphne Luchtenberg about how companies across industries and sizes can learn from leaders and integrate analytics and data to improve their operations. The following is an edited version of their conversation. Daphne Luchtenberg: Earlier this year, McKinsey and MIT's Machine Intelligence for Manufacturing and Operations studied 100 companies and sectors from automotive to mining. To discuss this and more, I'm joined by the authors, Vijay D'Silva, senior partner emeritus at McKinsey, and Bruce Lawler, managing director for MIT's MIMO. Let's start with the why.


Challenges of Artificial Intelligence -- From Machine Learning and Computer Vision to Emotional Intelligence

arXiv.org Artificial Intelligence

Artificial intelligence (AI) has become a part of everyday conversation and our lives. It is considered as the new electricity that is revolutionizing the world. AI is heavily invested in both industry and academy. However, there is also a lot of hype in the current AI debate. AI based on so-called deep learning has achieved impressive results in many problems, but its limits are already visible. AI has been under research since the 1940s, and the industry has seen many ups and downs due to over-expectations and related disappointments that have followed. The purpose of this book is to give a realistic picture of AI, its history, its potential and limitations. We believe that AI is a helper, not a ruler of humans. We begin by describing what AI is and how it has evolved over the decades. After fundamentals, we explain the importance of massive data for the current mainstream of artificial intelligence. The most common representations for AI, methods, and machine learning are covered. In addition, the main application areas are introduced. Computer vision has been central to the development of AI. The book provides a general introduction to computer vision, and includes an exposure to the results and applications of our own research. Emotions are central to human intelligence, but little use has been made in AI. We present the basics of emotional intelligence and our own research on the topic. We discuss super-intelligence that transcends human understanding, explaining why such achievement seems impossible on the basis of present knowledge,and how AI could be improved. Finally, a summary is made of the current state of AI and what to do in the future. In the appendix, we look at the development of AI education, especially from the perspective of contents at our own university.


Sony CSL develops AI tool to help musicians generate new melodies and chords

ZDNet

The computer science research arm of Sony has launched a cloud-based AI music production tool called Flow Machines Mobile (FM Mobile) to help musicians generate ideas for new melodies, chords, and basslines. The Sony Computer Science Laboratories (CSL) FM Mobile app, available on the Apple App store and compatible with a range of digital audio workstation (DAW), features a machine learning model that analyses musical data based on the style palette that users select to match the genre and chord progression of the song they want to create. Users can create their own original style palette in the app or choose from various preset palettes created by Sony CSL. When users press the compose button, the AI will generate eight-bar melodies according to the selected chord progression, Sony CSL said. "There are parameters such as note duration and melodic complexity, which allows users to have proposals from AI matching their intention," the company added.


Sony's head of AI research wants to build robots that can win a Nobel Prize

Engadget

AI and Machine Learning systems have proven a boon to scientific research in a variety of academic fields in recent years. They've assisted scientists in identifying genomic markers ripe for cutting-edge treatments, accelerating the discovery of potent new drugs and therapeutics, and even publishing their own research. Throughout this period, however, AI/ML systems have often been relegated to simply processing large data sets and performing brute force computations, not leading the research themselves. But Dr. Hiroaki Kitano, CEO of Sony Computer Science Laboratories, has plans for a "hybrid form of science that shall bring systems biology and other sciences into the next stage," by creating an AI that's just as capable as today's top scientific minds. To do so, Kitano seeks to launch the Nobel Turing Challenge and develop a AI smart enough to win itself a Nobel Prize by 2050.


Samsung to spend $205 billion across 3 years for 'strategic businesses'

ZDNet

Samsung said on Tuesday it will spend 240 trillion won, approximately $205 billion, over the next three years as part of efforts to become a leader in what it calls "strategically important industries". These industries include semiconductors, biopharmaceuticals, telecommunications, and emerging technologies such as AI and robotics. The 240 trillion won allocation will create 40,000 direct jobs, with 180 trillion won of that total to be spent in South Korea. According to Samsung, it spent around 180 trillion won in capital expenditure and R&D from 2018 to 2020. The upped spending plan for the next three years is aimed at making Samsung a leader for strategically important industries so it is ready for "great changes in industry, international order, and social structure expected after the COVID-19 pandemic," the South Korean tech giant said.


The Role of Social Movements, Coalitions, and Workers in Resisting Harmful Artificial Intelligence and Contributing to the Development of Responsible AI

arXiv.org Artificial Intelligence

There is mounting public concern over the influence that AI based systems has in our society. Coalitions in all sectors are acting worldwide to resist hamful applications of AI. From indigenous people addressing the lack of reliable data, to smart city stakeholders, to students protesting the academic relationships with sex trafficker and MIT donor Jeffery Epstein, the questionable ethics and values of those heavily investing in and profiting from AI are under global scrutiny. There are biased, wrongful, and disturbing assumptions embedded in AI algorithms that could get locked in without intervention. Our best human judgment is needed to contain AI's harmful impact. Perhaps one of the greatest contributions of AI will be to make us ultimately understand how important human wisdom truly is in life on earth.


Autonomous Driving, AI System on a Chip, Drug Discovery Firms Among Top Funded - AI Trends

#artificialintelligence

The top-funded companies on the recently-released list of top 100 most-promising AI companies to watch from CBInsights, a market intelligence company based in New York, include companies offering autonomous driving software, an AI System on a chip, endpoint security with AI, and a drug discovery company. The list, selected from a base of 6,000 companies, is based on business relations, investor profile, news sentiment analysis, R&D activity, a proprietary scoring system, market potential, competitive landscape, team strength and tech novelty, according to an account in TechRepublic. "This year's cohort spans 18 industries, and is working on everything from climate risk to accelerating drug R&D," stated CB Insights CEO Anand Sanwal. Companies on last year's list went on to raise $5.2 billion in additional financing, including 16 of over $100 million each. Some companies exited via merger or acquisition, IPOs or SPACS.


Machine Learning Panel Data Regressions with an Application to Nowcasting Price Earnings Ratios

arXiv.org Machine Learning

This paper introduces structured machine learning regressions for prediction and nowcasting with panel data consisting of series sampled at different frequencies. Motivated by the empirical problem of predicting corporate earnings for a large cross-section of firms with macroeconomic, financial, and news time series sampled at different frequencies, we focus on the sparse-group LASSO regularization. This type of regularization can take advantage of the mixed frequency time series panel data structures and we find that it empirically outperforms the unstructured machine learning methods. We obtain oracle inequalities for the pooled and fixed effects sparse-group LASSO panel data estimators recognizing that financial and economic data exhibit heavier than Gaussian tails. To that end, we leverage on a novel Fuk-Nagaev concentration inequality for panel data consisting of heavy-tailed $\tau$-mixing processes which may be of independent interest in other high-dimensional panel data settings.


Domain-Specific Hardware Accelerators

Communications of the ACM

From the simple embedded processor in your washing machine to powerful processors in data center servers, most computing today takes place on general-purpose programmable processors or CPUs. CPUs are attractive because they are easy to program and because large code bases exist for them. The programmability of CPUs stems from their execution of sequences of simple instructions, such as ADD or BRANCH; however, the energy required to fetch and interpret an instruction is 10x to 4000x more than that required to perform a simple operation such as ADD. This high overhead was acceptable when processor performance and efficiency were scaling according to Moore's Law.32 One could simply wait and an existing application would run faster and more efficiently. Our economy has become dependent on these increases in computing performance and efficiency to enable new features and new applications. Today, Moore's Law has largely ended,12 and we must look to alternative architectures with lower overhead, such as domain-specific accelerators, to continue scaling of performance and efficiency. There are several ways to realize domain-specific accelerators as discussed in the sidebar on accelerator options. A domain-specific accelerator is a hardware computing engine that is specialized for a particular domain of applications. Accelerators have been designed for graphics,26 deep learning,16 simulation,2 bioinformatics,49 image processing,38 and many other tasks. Accelerators can offer orders of magnitude improvements in performance/cost and performance/W compared to general-purpose computers. For example, our bioinformatics accelerator, Darwin,49 is up to 15,000x faster than a CPU at reference-based, long-read assembly. The performance and efficiency of accelerators is due to a combination of specialized operations, parallelism, efficient memory systems, and reduction of overhead. Domain-specific accelerators7 are becoming more pervasive and more visible, because they are one of the few remaining ways to continue to improve performance and efficiency now that Moore's Law has ended.22 Most applications require modifications to achieve high speed up on domain-specific accelerators. These applications are highly tuned to balance the performance of conventional processors with their memory systems.