Neural architecture search (NAS) deals with the selection of neural models for specific learning problems. NAS, however, is computationally expensive for automating and democratising machine learning. The initial success of NAS was attributed partially to the weight-sharing method, which helped in the dramatic acceleration of probing the architectures. But why is the weight sharing method being criticised?

Traditionally, NAS methods were expensive due to the combinatorially large search space, requiring to train thousands of neural networks to completion. In 2018, ENAS (Efficient NAS) paper, introduced the idea of weight-sharing, in which only one shared set of model parameters is trained for all architectures.

These shared weights were used to compute the validation losses of different architectures which are then used as estimates of their validation losses. Since one had to train only one set of parameters, weight-sharing led to a massive speedup over earlier methods, reducing search time on CIFAR-10 from 2,000-20,000 GPU-hours to just 16.

The validation accuracies computed using shared weights were sufficient to find good models cheaply. However, this correlation, although sufficient, doesnt mean that weight-sharing does well.

This method has come under scrutiny due to its poor performance as a substitute for full model-training and is alleged to be inconsistent with results on recent benchmarks.

The technique of sharing parameters among child models allowed efficient NAS to deliver strong empirical performances, whilst using much fewer GPU-hours than existing automatic model design approaches, and notably, 1000x less expensive than standard Neural Architecture.

The most popular implementation of shared weights as substitutes for standalone weights is the Random Search with Weight-Sharing (RS-WS) method, in which the shared parameters are optimised by taking gradient steps using architectures sampled uniformly at random from the search space.

However, practitioners started to wonder if sharing weights between models accelerate NAS.

In an attempt to address this issue and to make a case for the weight sharing mechanism, the researchers at CMU published a work that lists their findings. The paper states that most of the criticism on weight sharing has the issue of the rank disorder as a common occurrence. The rank disorder occurs when the shared-weight performance of architectures does not correlate well with their standalone performance.

The rank disorder is a problem for those methods, which rely on the shared-weights performance to rank architectures for evaluation, as it will cause them to ignore networks that achieve high accuracy when their parameters are trained without sharing.

Home What Is Weight Sharing In Deep Learning And Why Is It Important

The above picture illustrates rank-disorder issues where shared-weights are on the right, and individual weights trained from scratch are on the left.

To tackle this, the researchers present a unifying framework for designing and analysing gradient-based NAS methods that exploit the underlying problem structure to find high-performance architectures quickly. The geometry-aware framework, wrote the researchers, resulted in the algorithms that:

The results show that this new framework outclasses previous best works for both CIFAR and ImageNet on both the DARTS search space and NAS-Bench-201.

According to the authors, this work on weight sharing methods tried to establish the following:

Link to paper

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What Is Weight Sharing In Deep Learning And Why Is It Important - Analytics India Magazine

A recent survey by Kaggle revealed that data professionals used a variety of different algorithms, tools, frameworks and products to extract insights. Top algorithms were linear/logistic regression, decision trees/random forests and Gradient Boosting Machines. Top frameworks were Scikit-learn and TensorFlow. Top tools for automation were related to model selection and data augmentation. While half of the respondents did not use ML products, the top products used were Google Cloud ML Engine, Azure ML Studio and Amazon Sagemaker.

Machine learning is employed by data scientists to find patterns and predict important outcomes. The application of machine learning reaches across industries (e.g., healthcare, education) and professions (e.g., marketing, content management), and data professionals have many different tools, methods and products they can use to extract useful insights. Kaggle conducted a survey in October 2019 of nearly 20,000 data professionals (2019 Kaggle Machine Learning and Data Science Survey) that reveals the variety of ways they solve their machine learning problems. Todays post is about the machine learning methods and tools data professionals used in 2019.

Figure 1. Top Machine Learning Algorithms Used in 2019. Click image to enlarge.

The survey included a question for data professionals, Which of the following machine learning algorithms do you use on a regular basis? Select all that apply. On average, data professionals used 3 (median) machine learning algorithms. The top 10 machine learning algorithms used were (see Figure 1):

Adoption rates for the top two algorithms were the highest for data professionals who self-identified as statistician and data scientist. Adoption rates were around 10 percentage points higher for these data pros (e.g., ~80% for linear/logistic regression, ~70% for decision trees and random forests).

Arecent poll by KDNuggets found similar results to the current study. In their study, machine learning methods also included regression (56%), decision trees/rules (48%), random forests (45%), Gradient Boosting Machines (23%).

Figure 2. Machine Learning Frameworks Used. Click image to enlarge.

The survey included a question, Which of the following machine learning frameworks do you use on a regular basis? Select all that apply. On average, data professionals used 2 (median) machine learning frameworks. The top 10 machine learning frameworks used were (see Figure 2):

Figure 3. Machine Learning Tools Used. Click image to enlarge.

The survey also asked all data professionals about the machine learning tools they used. A little over half of the respondents (53%) indicated that they did not use any automated machine learning tools. The most used automated machine learning tool used were (see Figure 3):

Figure 4. Machine Learning Products Used. Click image to enlarge.

The survey also asked all data professionals about the machine learning products they used. A little over a third of the respondents (38%) indicated that they did not use any machine learning products. The most used automated machine learning products used were (see Figure 4):

I conducted a principal components analysis of all the various machine learning utilities to identify groupings of these machine learning methods. I found a fairly clear 9-component solution:

Azure Machine Learning Studio stood out as the lone product as it did not load on any of the 9 components.

The pattern of results show that the various machine learning methods tend to be used together. For example, when ML automation tools are used, data professionals tend to use all of them. Similarly, data professionals either tend use all Google products or use none of them. Data professionals who employ evolutionary approaches also tend to use generative adversarial networks.

The results of the Kaggle survey of nearly 20,000 data professionals reveals the most popular machine learning algorithms, products, tools and frameworks.

While machine learning is still a hot and growing field of data science, over a third of the respondents do not use any ML products. Top algorithms used were linear/logistic regression, decision trees/random forests and Gradient Boosting Machines. The most used machine learning frameworks were Scikit-learn and TensorFlow. Top tools for machine learning automation were related to model selection and data augmentation. The top products used were Google Cloud ML Engine, Azure ML Studio and Amazon Sagemaker.

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Top Machine Learning Algorithms, Frameworks, Tools and Products Used by Data Scientists - Customer Think

This detailed market study covers machine learning chip market growth potentials which can assist the stake holders to understand key trends and prospects in machine learning chip market identifying the growth opportunities and competitive scenarios. The report also focuses on data from different primary and secondary sources, and is analyzed using various tools. It helps to gain insights into the markets growth potential, which can help investors identify scope and opportunities. The analysis also provides details of each segment in the global machine learning chip market

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According to the report, the machine learning chip market report points out national and global business prospects and competitive conditions for machine learning chip. Market size estimation and forecasts were given based on a detailed research methodology tailored to the conditions of the demand for machine learning chip. The machine learning chip market has been segmented by chip type (gpu, asic, fpga, cpu, others), by technology (system-on-chip, system-in-package, multi-chip module, others), by industry vertical (media & advertising, bfsi, it & telecom, retail, healthcare, automotive & transportation, others). Historical background for the demand of machine learning chip has been studied according to organic and inorganic innovations in order to provide accurate estimates of the market size. Primary factors influencing the growth of the demand machine learning chip have also been established with potential gravity.

Regional segmentation and analysis to understand growth patterns:The market has been segmented in major regions to understand the global development and demand patterns of this market.

By region, the machine learning chip market has been segmented in North America, Europe, Asia Pacific, Middle East, and Rest of the World. The North America and Western Europe regions are estimated to register a stable demand during the forecast period with market recovery from recent slowdowns. North America region includes the US, Canada, and Mexico. The US is estimated to dominate this market with a sizeable share followed by Canada, and Mexico. The industrial sector is a major contributor to the US and Canada economies overall. Hence, the supply of advanced materials in production activities is critical to the overall growth of industries in this region.

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Western Europe region is dominated by Germany, the UK, France, Italy, and Spain. These countries also have a strong influence on the industrial sector resulting in sizeable demand for machine learning chip market . Asia Pacific is estimated to register the highest CAGR by region during the forecast period.

The presence of some of the high growth economies such as China and India is expected to propel the demand in this region. Besides, this region has witnessed strategic investments by major companies to increase their market presence. The Middle East and Eastern Europe are estimated to be other key regions for the machine learning chip market with a strong market potential during the forecast period. Rest of the World consisting of South America and Africa are estimated to be emerging markets during the forecast period.

This report provides:1) An overview of the global market for machine learning chip market and related technologies.2) Analysis of global market trends, yearly estimates and annual growth rate projections for compounds (CAGRs).3) Identification of new market opportunities and targeted consumer marketing strategies for global machine learning chip market .4) Analysis of R&D and demand for new technologies and new applications5) Extensive company profiles of key players in industry.

The researchers have studied the market in depth and have developed important segments such as product type, application and region. Each and every segment and its sub-segments are analyzed based on their market share, growth prospects and CAGR. Each market segment offers in-depth, both qualitative and quantitative information on market outlook.

With an emphasis on strategies there have been several primary developments done by major companies such as AMD (Advanced Micro Devices), Google Inc., Intel Corporation, NVIDIA, Baidu, Bitmain Technologies, Qualcomm, Amazon, Xilinx, Samsung.

Market Segmentation:By Chip Type:o GPUo ASICo FPGAo CPUo Others

By Technology:o System-on-chipo System-in-packageo Multi-chip moduleo Others

By Industry Vertical:o Media & Advertisingo BFSIo IT & Telecomo Retailo Healthcareo Automotive & Transportationo Others

By Region:North America Machine Learning Chip Marketo North America, by Countryo USo Canadao Mexicoo North America, by Chip Typeo North America, by Technologyo North America, by Industry Vertical

Europe Machine Learning Chip Marketo Europe, by Countryo Germanyo Russiao UKo Franceo Italyo Spaino The Netherlandso Rest of Europeo Europe, by Chip Typeo Europe, by Technologyo Europe, by Industry Vertical

Asia Pacific Machine Learning Chip Marketo Asia Pacific, by Countryo Chinao Indiao Japano South Koreao Australiao Indonesiao Rest of Asia Pacifico Asia Pacific, by Chip Typeo Asia Pacific, by Technologyo Asia Pacific, by Industry Vertical

Middle East & Africa Machine Learning Chip Marketo Middle East & Africa, by Countryo UAEo Saudi Arabiao Qataro South Africao Rest of Middle East & Africao Middle East & Africa, by Chip Typeo Middle East & Africa, by Technologyo Middle East & Africa, by Industry Vertical

South America Machine Learning Chip Marketo South America, by Countryo Brazilo Argentinao Colombiao Rest of South Americao South America, by Chip Typeo South America, by Technologyo South America, by Industry Vertical

Reasons to Buy This Report:o Provides niche insights for decision about every possible segment helping in strategic decision making process.o Market size estimation of the machine learning chip market on a regional and global basis.o A unique research design for market size estimation and forecast.o Identification of major companies operating in the market with related developmentso Exhaustive scope to cover all the possible segments helping every stakeholder in the machine learning chip

Customization:This study is customized to meet your specific requirements:o By Segmento By Sub-segmento By Region/Countryo Product Specific Competitive Analysis

Contact:Quince Market InsightsAjay D. (Knowledge Partner)Office No- A109Pune, Maharashtra 411028Phone: US +1 208 405 2835 UK +44 121 364 6144 APAC +91 706 672 4848Email:[emailprotected]Web:www.quincemarketinsights.com

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Machine Learning Chip Market Growth Accelerated by Healthy CAGR, Upcoming Trends and Key Companies Analysis | AMD (Advanced Micro Devices), Google...

The 'Artificial Intelligence and Machine Learning Industry market' research added by Market Study Report, LLC, offers a comprehensive analysis of growth trends prevailing in the global business domain. This report also provides definitive data concerning market, size, commercialization aspects and revenue forecast of the industry. In addition, the study explicitly highlights the competitive status of key players within the projection timeline while focusing on their portfolio and regional expansion endeavors.

The Artificial Intelligence and Machine Learning Industry market report is an in-depth analysis of this business space. The major trends that defines the Artificial Intelligence and Machine Learning Industry market over the analysis timeframe are stated in the report, along with additional pointers such as industry policies and regional industry layout. Also, the report elaborates on the impact of existing market trends on investors.

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COVID-19, the disease it causes, surfaced in late 2019, and now had become a full-blown crisis worldwide. Over fifty key countries had declared a national emergency to combat coronavirus. With cases spreading, and the epicentre of the outbreak shifting to Europe, North America, India and Latin America, life in these regions has been upended the way it had been in Asia earlier in the developing crisis. As the coronavirus pandemic has worsened, the entertainment industry has been upended along with most every other facet of life. As experts work toward a better understanding, the world shudders in fear of the unknown, a worry that has rocked global financial markets, leading to daily volatility in the U.S. stock markets.

Other information included in the Artificial Intelligence and Machine Learning Industry market report is advantages and disadvantages of products offered by different industry players. The report enlists a summary of the competitive scenario as well as a granular assessment of downstream buyers and raw materials.

Revealing a gist of the competitive landscape of Artificial Intelligence and Machine Learning Industry market:

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An outlook of the Artificial Intelligence and Machine Learning Industry market regional scope:

Additional takeaways from the Artificial Intelligence and Machine Learning Industry market report:

This report considers the below mentioned key questions:

Q.1. What are some of the most favorable, high-growth prospects for the global Artificial Intelligence and Machine Learning Industry market?

Q.2. Which products segments will grow at a faster rate throughout the forecast period and why?

Q.3. Which geography will grow at a faster rate and why?

Q.4. What are the major factors impacting market prospects? What are the driving factors, restraints, and challenges in this Artificial Intelligence and Machine Learning Industry market?

Q.5. What are the challenges and competitive threats to the market?

Q.6. What are the evolving trends in this Artificial Intelligence and Machine Learning Industry market and reasons behind their emergence?

Q.7. What are some of the changing customer demands in the Artificial Intelligence and Machine Learning Industry Industry market?

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Artificial Intelligence and Machine Learning Industry Market to Attain a Valuation of Highest CAGR 2020-2025 - AlgosOnline

SEO technology is taking a new shape with machine learning.

FREMONT, CA: Machine learning technology is making strides in the world of business today. Lately, search engine optimization (SEO) is amalgamating with machine learning to bring in intelligence and automation. The ever-increasing pressure on the search analysts and the volumes of search content recorded by the search engines across the world is making it a hard task for the SEO technologists to reach further milestones. The need for intelligence is rising, and the best way to fulfill this is with the use of machine-level smartness. This is exactly where the value of machine learning transfigures SEO.

The role of machine learning in search algorithms helps in rationalizing the complexities involved in the search. Also, this technology helps in bettering the performance of a search engine. Tools based on machine learning always account to equating the relevance of the context with that of what is being searched. The relationship of the searched words with the result, and the meaning of the significances is also taken care of by ML, which would otherwise require human intelligence to sort and define such relevance.

Data science has also made way into SEO. Marketers are seeking miscellaneous benefits from using the concepts of data science. Catering to the marketing era that is driven by results, machine learning serves as specialized support to the SEO professionals and empowers them to rank the site high on the search engine instantly. Algorithms of data science merge with those of SEO and give digital marketers an idea about the market analysis. Machine learning generates actionable insights into search volume, cost per click, audience base, views, number of searches, and other parameters and presents them to the marketers and SEO analysts in real-time.

Machine learning guides the SEO industry toward becoming smarter instantly.

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What's the Impact of Machine Learning on SEO? - Marketing Tech Outlook

Global Machine Learning as a Service (MLaaS) Market 2020 by Company, Regions, Type and Application, Forecast to 2025 is specialized and in-depth industry research dealing with all technical and profitable business outlook. The report offers market share analysis in terms of volumes during the forecast period from 2020 to 2025. The report scrutinizes the market by an exhaustive analysis of global Machine Learning as a Service (MLaaS) market size, market dynamics, current trends, challenges, issues, competition analysis, and companies involved. The report monitors the key trends and market drivers in the current scenario and offers on-the-ground insights. It further covers product classification, growth rate, product price, and product up gradation and innovations.

Key players are concentrating on extending their footprints across key regions. Players profiled: Microsoft, At&T, Google, International Business Machine, Hewlett-Packard Enterprise Development, Amazon Web Services, Fico, Bigml

The study focuses on the leading players of the global Machine Learning as a Service (MLaaS) market combined with various depending aspects related to the market as well as their profiles are analyzed emphatically by landscape contrast. All the historical and current trends of the market are discussed comprehensively in the report. Next, the report studies the factors responsible for hindering and enhancing growth in the industry. Then the report aims to provide how the market will grow during the forecast period. The report provides granular, robust qualitative data on how the market is changing and quantitative market outlooks.

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NOTE: Our analysts monitoring the situation across the globe explains that the market will generate remunerative prospects for producers post COVID-19 crisis. The report aims to provide an additional illustration of the latest scenario, economic slowdown, and COVID-19 impact on the overall industry.

The report offers examination and growth of the market in these districts covering North America (United States, Canada and Mexico), Europe (Germany, France, UK, Russia and Italy), Asia-Pacific (China, Japan, Korea, India, Southeast Asia and Australia), South America (Brazil, Argentina, Colombia), Middle East and Africa (Saudi Arabia, UAE, Egypt, Nigeria and South Africa).

Market segment by type covers: Special Service, Management Services,

Market segment by applications can be divided into: Banking, Financial Services, Insurance, Automobile, Health Care, Defense, Retail, Media& Entertainment, Communication,

The Basic Market Drivers, Challenges, And Strategies Adopted:

The report covers detailed information regarding the major factors affecting the growth of the Machine Learning as a Service (MLaaS) market such as drivers, threats, entry barriers, obstacles, challenges, and opportunities. There is also an estimate of how much this line of business will be worth at the end of the forecast period. At the same time, there will be a focus on what drives the popularity of these types of products or services. The competitive approach offers a robust judgment to the reader that can aid to form own business policies and strategies.

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The global Machine Learning as a Service (MLaaS) market report effectively provides required features of the global market for the population and for the business looking people for mergers & acquisitions, making investments, new vendors. Additionally, economic scenarios with the item value, benefit, supply, limit, generation, request, market development rate, and figure have been given in the report.

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Global Machine Learning as a Service (MLaaS) Market 2020 Analysis, Types, Applications, Forecast and COVID-19 Impact Analysis 2025 - Owned

LONDON--(BUSINESS WIRE)--Technavio has been monitoring the machine learning market and it is poised to grow by $ 11.16 bn during 2020-2024, progressing at a CAGR of about 39% during the forecast period. The report offers an up-to-date analysis regarding the current market scenario, latest trends and drivers, and the overall market environment.

Technavio suggests three forecast scenarios (optimistic, probable, and pessimistic) considering the impact of COVID-19. Please Request Latest Free Sample Report on COVID-19 Impact

The market is fragmented, and the degree of fragmentation will accelerate during the forecast period. Alibaba Group Holding Ltd., Alphabet Inc., Amazon.com Inc., Cisco Systems Inc., Hewlett Packard Enterprise Development LP, International Business Machines Corp., Microsoft Corp., Salesforce.com Inc., SAP SE, and SAS Institute Inc. are some of the major market participants. To make the most of the opportunities, market vendors should focus more on the growth prospects in the fast-growing segments, while maintaining their positions in the slow-growing segments.

The increasing adoption of cloud-based offerings has been instrumental in driving the growth of the market. However, the shortage of skilled personnel might hamper market growth.

Machine learning market 2020-2024 : Segmentation

Machine learning market is segmented as below:

To learn more about the global trends impacting the future of market research, download a free sample: https://www.technavio.com/talk-to-us?report=IRTNTR40918

Machine learning market 2020-2024 : Scope

Technavio presents a detailed picture of the market by the way of study, synthesis, and summation of data from multiple sources. Our machine learning market report covers the following areas:

This study identifies the increasing use of machine learning in customer experience management as one of the prime reasons driving the machine learning market growth during the next few years.

Machine learning market 2020-2024 : Vendor Analysis

We provide a detailed analysis of around 25 vendors operating in the machine learning market, including some of the vendors such as Alibaba Group Holding Ltd., Alphabet Inc., Amazon.com Inc., Cisco Systems Inc., Hewlett Packard Enterprise Development LP, International Business Machines Corp., Microsoft Corp., Salesforce.com Inc., SAP SE, and SAS Institute Inc. Backed with competitive intelligence and benchmarking, our research reports on the machine learning market are designed to provide entry support, customer profile and M&As as well as go-to-market strategy support.

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Machine learning market 2020-2024 : Key Highlights

Table Of Contents :

Executive Summary

Market Landscape

Market Sizing

Five Forces Analysis

Market Segmentation by End-user

Customer Landscape

Geographic Landscape

Drivers, Challenges, and Trends

Vendor Landscape

Vendor Analysis

Appendix

About Us

Technavio is a leading global technology research and advisory company. Their research and analysis focuses on emerging market trends and provides actionable insights to help businesses identify market opportunities and develop effective strategies to optimize their market positions. With over 500 specialized analysts, Technavios report library consists of more than 17,000 reports and counting, covering 800 technologies, spanning across 50 countries. Their client base consists of enterprises of all sizes, including more than 100 Fortune 500 companies. This growing client base relies on Technavios comprehensive coverage, extensive research, and actionable market insights to identify opportunities in existing and potential markets and assess their competitive positions within changing market scenarios.

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COVID-19 Impacts: Machine Learning Market will Accelerate at a CAGR of about 39% through 2020-2024 | The Increasing Adoption of Cloud-based Offerings...

Its not surprising that there have been many intersections of artificial intelligence and machine learning with the current coronavirus epidemic. AI and ML are very hot topics indeed, not least because they hold out the promise of sudden insights that would be hard to obtain by normal means. Sounds like something were in need of in the current situation, doesnt it? So there have been reports of using these techniques to repurpose known drugs, to sort through virtual compound libraries and to generate new structures, to try to optimize treatment regimes, to recommend antigen types for vaccine development, and no doubt many more.

Ive been asked many times over the last few months what I think about all this, and Ive written about some of this. And Ive also written about AI and machine learning in general, and quite a few times. But let me summarize and add a few more thoughts here.

The biggest point to remember, when talking about AI/ML and drug discovery, is that these techniques will not help you if you have a big problem with insufficient information. They dont make something from nothing. Instead, they sort through huge piles of Somethings in ways that you dont have the resources or patience to do yourself. That means (first) that you must be very careful about what you feed these computational techniques at the start, because garbage in, garbage out has never been more true than it is with machine learning. Indeed, data curation is a big part of every successful ML effort, for much the same reason that surface preparation is a big part of every successful paint job.

And second, it means that there is a limit on what you can squeeze out of the information you have. What if youve curated everything carefully, and the pile of reliable data still isnt big enough? Thats our constant problem in drug research. There are just a lot of things that we dont know, and sometimes we are destined to find out about them very painfully and expensively. Look at that oft-quoted 90% failure rate across clinical trials: is that happening because people are lazy and stupid and enjoy shoveling cash into piles and lighting it on fire? Not quite: its generally because we keep running into things that we didnt know about. Whoops, turns out Protein XYZ is not as important as we thought in Disease ABC the patients dont really get much better. Or whoops, turns out that drugs that target the Protein XYZ pathway also target other things that we had never seen before and that cause toxic effects, and the patients actually get worse. No one would stumble into things like that on purpose. Sometimes, in hindsight, we can see how such things might have been avoided, but often enough its just One of Those Things, and we add a bit more knowledge to the pile, at great expense.

So when I get asked about things like GPT3, which has been getting an awful lot of press in recent months, thats my first thought. GPT3 handles textual information and looks for patterns and fill-in-the-blank opportunities, and for human language applications we have the advantage of being able to feed gigantic amounts of such text into it. Now, not all of that text might be full of accurate information, but it was all written with human purpose and some level of intelligence, and with intent to convey information to its readers, and man, does that ever count for a lot. Compare that to the data we get from scientific observation, which comes straight from the source, as it were, without the benefit of having been run through human brains first. As Ive pointed out before, for example, a processing chip or a huge pile of software code may appear dauntingly complex, but they were both designed by humans and other humans therefore have huge advantage when it comes to understanding them. Now look at the physical wiring of neurons in a human brain hell, look at the wiring in the brain of a fruit fly or the biochemical pathways involved in gene transcription, or the cellular landscape of the human immune system. Theyre different, fundamentally different, because a billion years of evolutionary tinkering will give you wonderously strange things that are under no constraints to be understandable to anything.

GPT3 can be made to do all sorts of fascinating things, if you can find a way to translate your data into something like text. Its the same way that we try to turn text into vector representations for other computational purposes; you transform your material (if you can) into something thats best suited for the tools you have at hand. A surprising number of things can be text-ified, and we have yet another advantage that this process has already been useful for other purposes besides modern-day machine learning. Here, for example, is an earlier version of the program (GPT2) being used on text representations of folk songs, in order to rearrange them into new folk songs (I suspect that it would be even easier to generate college football fight songs, but perhaps theres not as much demand for those). You can turn images into long text strings, too, and turn the framework loose on them, withinteresting results.

But what happens if you feed a pile of (say) DNA sequence information into GPT3? Will it spit out plausible gene sequences for interesting new kinase enzymes or microtubule-associated proteins? I doubt it. In fact, I doubt it a lot, but I would be very happy to hear about anyone whos tried it. Human writing, images that humans find useful or interesting, and human music already have our fingerprints all over them, but genomic sequences, well. . .they have a funkiness that is all their own. There are things that Im sure the program could pick out, but Id like to know how far that extends.

And even if it really gets into sequences, itll hit a wall pretty fast. Theres a lot more to a single living cell than its gene sequence; thats one lesson that have had should have had beaten into our heads over and over. Now consider how much more there is to an entire living organism. Im all for shoveling in DNA sequences, RNA sequences, protein sequences, three-dimensional protein structures, everything else that we can push in through the textual formatting slot, to see what the technology can make of it. But again, thats only going to take you so far. There are feedback loops, networks of signaling, constantly shifting concentrations and constantly shifting spatial arrangements inside every cell, every tissue, every creature that are all interconnected in ways that, lets state again, we have not figured out. There are no doubt important things that can be wrung out of the (still massive) amount of information that we have, and Ill for finding them. But if you revved up the time machine and sent a bunch of GPT-running hardware (or any other back to 1975 (or 2005, for that matter) it would not have predicted the things about cell biology and disease that weve discovered since then. Those things, with few exceptions, werent latent in the data we had then. We needed more. We still do.

Apply this to the coronavirus pandemic, and the problems become obvious. We dont know what levels of antibodies (or T cells) are protective, how long such protection might last, and how it might vary among cohorts and individuals. We have been discovering major things about transmissibility by painful experience. We have no good idea about why some people become much sicker than others (once you get past a few major risk factors, age being the main one), or why some organ systems get hit in some patients and not in others. And so very much on these are limits of our knowledge, and no AI platform will fill those in for us.

From what I understand, the GPT3 architecture might already be near its limits, anyway. But there will be more ML programs and better ones, thats for sure. Google, for example, has just published a very interesting paper which is all about using machine learning to improve machine learning algorithms. I suspect that I am not the only old science-fiction fan who thought of this passage from William Gibsons Neuromancer on reading this:

Autonomy, thats the bugaboo, where your AIs are concerned. My guess, Case, youre going in there to cut the hard-wired shackles that keep this baby from getting any smarter. And I cant see how youd distinguish, say, between a move the parent company makes, and some move the AI makes on its own, so thats maybe where the confusion comes in. Again the non laugh. See, those things, they can work real hard, buy themselves time to write cookbooks or whatever, but the minute, I mean the nanosecond, that one starts figuring out ways to make itself smarter, Turingll wipe it. . .Every AI ever built has an electromagnetic shotgun wired to its forehead.

Were a long way from the world of Neuromancer probably a good thing, too, considering how the AIs behave in it. The best programs that we are going to be making might be able to discern shapes and open patches in the data we give them, and infer that there must be something important there that is worth investigating, or be able to say If there were a connection between X and Y here, everything would make a lot more sense maybe see if theres one we dont know about. Ill be very happy if we can get that far. We arent there now.

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AI, Machine Learning and the Pandemic | In the Pipeline - Science Magazine

I just co-authored a paper on the legal risks of doing machine learning research, given the current state of the Computer Fraud and Abuse Act:

Abstract: Adversarial Machine Learning is booming with ML researchers increasingly targeting commercial ML systems such as those used in Facebook, Tesla, Microsoft, IBM, Google to demonstrate vulnerabilities. In this paper, we ask, What are the potential legal risks to adversarial ML researchers when they attack ML systems? Studying or testing the security of any operational system potentially runs afoul the Computer Fraud and Abuse Act (CFAA), the primary United States federal statute that creates liability for hacking. We claim that Adversarial ML research is likely no different. Our analysis show that because there is a split in how CFAA is interpreted, aspects of adversarial ML attacks, such as model inversion, membership inference, model stealing, reprogramming the ML system and poisoning attacks, may be sanctioned in some jurisdictions and not penalized in others. We conclude with an analysis predicting how the US Supreme Court may resolve some present inconsistencies in the CFAAs application in Van Buren v. United States, an appeal expected to be decided in 2021. We argue that the court is likely to adopt a narrow construction of the CFAA, and that this will actually lead to better adversarial ML security outcomes in the long term.

Medium post on the paper. News article, which uses our graphic without attribution.

*** This is a Security Bloggers Network syndicated blog from Schneier on Security authored by Bruce Schneier. Read the original post at: https://www.schneier.com/blog/archives/2020/07/adversarial_mac_1.html

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Adversarial Machine Learning and the CFAA - Security Boulevard

Trusted Business Insights answers what are the scenarios for growth and recovery and whether there will be any lasting structural impact from the unfolding crisis for the Machine Learning market.

Trusted Business Insights presents an updated and Latest Study on Machine Learning Market 2019-2026. The report contains market predictions related to market size, revenue, production, CAGR, Consumption, gross margin, price, and other substantial factors. While emphasizing the key driving and restraining forces for this market, the report also offers a complete study of the future trends and developments of the market.The report further elaborates on the micro and macroeconomic aspects including the socio-political landscape that is anticipated to shape the demand of the Machine Learning market during the forecast period (2019-2029).It also examines the role of the leading market players involved in the industry including their corporate overview, financial summary, and SWOT analysis.

Get Sample Copy of this Report @ Machine Learning Market Size, Share, Global Market Research and Industry Forecast Report, 2025 (Includes Business Impact of COVID-19)

Industry Insights, Market Size, CAGR, High-Level Analysis: Machine Learning Market

The global machine learning market size was valued at USD 6.9 billion in 2018 and is anticipated to register a CAGR of 43.8% from 2019 to 2025. Emerging technologies such as artificial intelligence are changing the way industries and humans work. These technologies have optimized supply chains, launched various digital products and services, and transformed overall customer experience. Various tech firms are investing in this filed to develop AI platforms, while various startups are focusing on niche domain solutions. With this rapid development, AI techniques such as machine learning are gaining significant traction in the market.Machine learning is a subset of artificial intelligence. The concept has evolved from computational learning and pattern recognition in artificial intelligence. It explores the construction and study of algorithms and carries out forecasts on data. The applications of machine learning include e-mail filtering, Optical Character Recognition (OCR), detection of network intruders, computer vision, and learning to rank.

The technology has paved the way across various applications. In advertising, this technology is used to predict the behavior of a customer and helps in improving advertising campaigns. AI-driven marketing uses various models to optimize, automate, and augment the data into actions. In the case of banking and finance, loan approval, assets management, and other processes are carried out using machine learning. Other applications, such as security, document management, and publishing, are also using this technology, thereby driving the market.Recently, machine learning has made its way into new aspects. For instance, the U.S. Army is planning to use this technology in combat vehicles for predictive maintenance. It will help in determining repair and service required in these vehicles with details such as when and where the repair is required. The stock market is also making use of this technology in market prediction with an accuracy level of approximately 60%.

Component Insights of Machine Learning Market

Based on component, the market is divided into hardware, software, and services. The hardware segment is expected to register the highest CAGR over the forecast period. This can be attributed to growing adoption of hardware optimized for machine learning. Development of customized silicon chips with AI and ML capabilities is driving the adoption of hardware. Development of more powerful processing devices by companies such as SambaNova Systems are anticipated to further drive the market.The software segment is expected to account for a moderate share in the market. The adoption of cloud-based software is anticipated to rise due to enhanced cloud infrastructure and hosting parameters. Cloud-based software allows users to move from machine to deep learning, thereby driving adoption. Demand for machine learning services has been on a rise in recent years. Managed services help customers manage their ML tools and deal with varied dependency stacks.Enterprise Size InsightsBased on enterprise size, the machine learning market is categorized into Small and Medium Enterprises (SMEs) and large enterprises. The large enterprise segment accounted for the leading share in the market in 2018. This is due to increasing adoption of technologies such as artificial intelligence and data science to inject predictive insights into business operations. Large organizations are focusing on harnessing deep learning, machine learning, and optimization of decisions in order to deliver high business value.The adoption of machine learning is rapidly increasing among small and medium-sized enterprises. This is owing to easy and cost-effective deployment offered by machine learning. Availability of deployment options such as on cloud, on-premise, or hybrid allows SMEs to easily scale up their growing pilot projects and artificial intelligence initiatives, eliminating the need for large up-front investments.End-use InsightsBased on end use, the market is categorized into BFSI, healthcare, retail, law, advertising and media, agriculture, manufacturing, automotive and transportation, and others. While advertising and media held the leading share in 2018, the healthcare sector is expected to surpass this segment to account for the largest share by the end of the forecast period. This is due to rising adoption of this technology in emerging healthcare areas. For instance, this technology is being used to predict the probability of death of a person. Use of machine learning for quantitative insights for better diagnosis and using it to prevent diseases is moving the field of medicine from reactive to proactive and this is poised to drive the market.

The law segment is expected to register the highest CAGR over the forecast period. This is due to rising adoption of machine learning algorithms across various legal applications. In case of litigation, ML is used for continuous active learning for the process of document review. Due diligence analysis in the merger and acquisition process is done using ML. Privacy, information governance, expert systems, and client collaboration are some of the emerging legal areas that are adopting machine learning.

Regional Insights of Machine Learning Market

The market in North America held the dominant share in 2018, thanks to numerous banking organizations in the region investing in ML-based firms. For instance, in November 2019, JPMorgan Chase & Co. announced its investment in Limeglass, a provider of AI, ML, and NLP to analyze institutional research. The latter company is expected to assist emerging technology companies in developing various products required for banking.Asia Pacific is anticipated to register the highest CAGR over the forecast period. This is due to growing adoption of machine learning in emerging markets with a massive talent base, such as India. Greater access to consumers who are willing to try AI-enabled services and products is further driving the regional market. In May 2018, NITI Aayog, a policy think tank of the Government of India, collaborated with Google LLC, a multinational technology company. Through this collaboration, the former company will incubate and train start-ups based on AI in India.

Market Share Insights of Machine Learning Market

Key industry participants include Amazon Web Services, Inc.; Baidu Inc.; Google Inc.; H2O.ai; Intel Corporation; International Business Machines Corporation; Hewlett Packard Enterprise Development LP; Microsoft Corporation; SAS Institute Inc.; and SAP SE. Several vendors are entering into partnerships with end-use industries to enhance their reach. For instance, Microsoft Corporation partnered with LV Prasad Eye Institute in Hyderabad. This partnership is aimed at enabling machine learning to bring data-driven eye care services in India. Vendors are also focusing on launching new products in the market. For instance, International Business Machines Corporations machine learning technology advances the early detection of diabetic eye disease using deep learning.

Segmentations, Sub Segmentations, CAGR, & High-Level Analysis overview of Machine Learning Market Research ReportThis report forecasts revenue growth at global, regional, and country levels and provides an analysis of the latest industry trends in each of the sub-segments from 2014 to 2025. For the purpose of this study, this market research report has segmented the global machine learning market report based on component, enterprise size, end use, and region:

Component Outlook (Revenue, USD Million, 2019 2030)

Hardware

Software

Services

Enterprise Size Outlook (Revenue, USD Million, 2019 2030)

SMEs

Large Enterprises

End-use Outlook (Revenue, USD Million, 2019 2030)

Healthcare

BFSI

Law

Retail

Advertising & Media

Automotive & Transportation

Agriculture

Manufacturing

Others

Quick Read Table of Contents of this Report @ Machine Learning Market Size, Share, Global Market Research and Industry Forecast Report, 2025 (Includes Business Impact of COVID-19)

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How COVID-19 Pandemic Will Impact Machine Learning Market Business Opportunity, And Growth 2020-2026 - Jewish Life News