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Hear how three startups are approaching quantum computing differently at TC Disrupt 2020 – TechCrunch

Posted on July 23, 2020 by admin

Quantum computing is at an interesting point. Its at the cusp of being mature enough to solve real problems. But like in the early days of personal computers, there are lots of different companies trying different approaches to solving the fundamental physics problems that underly the technology, all while another set of startups is looking ahead and thinking about how to integrate these machines with classical computers and how to write software for them. At Disrupt 2020 on September 14-18, we will have a panel with D-Wave CEO Alan Baratz, Quantum Machines co-founder and CEO Itamar Sivan and IonQ president and CEO Peter Chapman. The leaders of these three companies are all approaching quantum computing from different angles, yet all with the same goal of making this novel technology mainstream.

D-Wave may just be the best-known quantum computing company thanks to an early start and smart marketing in its early days. Alan Baratz took over as CEO earlier this year after a few years as chief product officer and executive VP of R&D at the company. Under Baratz, D-Wave has continued to build out its technology and especially its D-Wave quantum cloud service. Leap 2, the latest version of its efforts, launched earlier this year. D-Waves technology is also very different from that of many other efforts thanks to its focus on quantum annealing. That drew a lot of skepticism in its early days but its now a proven technology and the company is now advancing both its hardware and software platform.

Like Baratz, IonQs Peter Chapman isnt a founder either. Instead, he was the engineering director for Amazon Prime before joining IonQ in 2019. Under his leadership, the company raised a $55 million funding round in late 2019, which the company extended by another $7 million last month. He is also continuing IonQs bet on its trapped ion technology, which makes it relatively easy to create qubits and which, the company argues, allows it to focus its efforts on controlling them. This approach also has the advantage that IonQs machines are able to run at room temperature, while many of its competitors have to cool their machines to as close to zero Kelvin as possible, which is an engineering challenge in itself, especially as these companies aim to miniaturized their quantum processors.

Quantum Machines plays in a slightly different part of the ecosystem from D-Wave and IonQ. The company, which recently raised $17.5 million in a Series A round, is building a quantum orchestration platform that combines novel custom hardware for controlling quantum processors because once quantum machines reach a bit more maturity, a standard PC wont be fast enough to control them with a matching software platform and its own QUA language for programming quantum algorithms.Quantum Machines is Itamar Sivans first startup, which he launched with his co-founders after getting his Ph.D. in condensed matter and material physics at the Weizman Institute of Science.

Come to Disrupt 2020 and hear from these companies and others on September 14-18. Get a front-row seat with your Digital Pro Pass for just $245 or with a Digital Startup Alley Exhibitor Package for $445. Prices are increasing next week, so grab yours today to save up to $300.

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Hear how three startups are approaching quantum computing differently at TC Disrupt 2020 - TechCrunch

Pros and Cons to Buying Microsoft (MSFT) Stock – WTOP

Posted on July 23, 2020 by admin

Twenty years ago, Microsoft Corp. (ticker: MSFT) was the most valuable company in the world. Today, along with competitors Apple

Twenty years ago, Microsoft Corp. (ticker: MSFT) was the most valuable company in the world.

Today, along with competitors Apple ( AAPL) and Amazon.com ( AMZN), Microsoft is worth around $1.5 trillion, and tech titans like Alphabet ( GOOG, GOOGL), Facebook ( FB) and Netflix ( NFLX) are not too far behind. The company that brought you Windows may not be alone at the top anymore, but Microsoft is far from obsolete and continues to remain relevant in markets around the world.

Is Microsoft stock still a buy in mid-2020? Heres a look at the biggest pros and cons associated with MSFT.

Microsoft Stock at a Glance

Rising to prominence in the late 1970s and early 1980s, Microsofts software became the industry standard for early PCs made by the likes of IBM ( IBM) and Apple. This gave Microsoft a crucial first-mover advantage.

By the 1990s, computers became small enough and economical enough for the average American household or typical elementary school to afford one. The end market wasnt just corporations and academia anymore, propelling Microsoft further.

As home computers became commonplace, so too was the operating system they used: Windows, the pre-installed, Microsoft-made software. Consumers loved the Windows user experience and its practical capabilities, especially the Microsoft Office suite of applications such as Word, Excel and PowerPoint.

By earning a hefty licensing fee on each computer sold with Windows and Office, Microsoft was able to achieve previously unimaginable scale over a short period.

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A few decades later and Windows is still a major cash cow for Microsoft. But the company has also been able to diversify, and its most exciting future growth prospects are expected to come from other areas like cloud computing, social networking, remote work apps and video games.

Pros of Buying Microsoft Stock

There have been three CEOs since Microsoft was founded in 1975: co-founder Bill Gates (1975-2000), Steve Ballmer (2000-2014) and Satya Nadella. Gates tenure was characterized by a company that experienced virtually unprecedented growth, making him the richest person in the world by the 1990s. Ballmers tenure was a struggle, as Microsoft failed to stay at the forefront of tech, largely missing the boat on huge growth industries it was perfectly positioned to dominate like smartphones, search engines and social networks.

Since 2014, Microsoft has been led by Nadella, a period that thus far has been characterized by a return to Wall Street prominence, outperformance, revenue diversification and its biggest theme: cloud computing.

Today, one of Microsofts biggest pros is essentially the same as what it was 20 years ago: The company has an unbelievable moat, a high barrier to entry. Many users around the world have learned everything they know about computers using Microsofts Windows operating system.

If you dont have an Apple computer, Windows is by far the operating system of choice for manufacturers and consumers alike, holding the majority of the desktop market share worldwide.

But Microsoft doesnt have to release a new version of Windows just to profit from Windows. In the fourth quarter of its fiscal 2020 year, Microsofts Windows original equipment manufacturer (OEM) revenue increased by 7%.

[READ 2020s Dividend Aristocrats List: All 66 Stocks]

Amid the pandemic, the tech companys Windows OEM non-Pro revenue increased 34% thanks to consumer demand driven by remote work needs for employees staying out of the office and remote learning scenarios, illustrating the strength of the Windows brand and the value of the product.

Windows is part of a business segment Microsoft labels More Personal Computing, and the segment also accounts for the Xbox and associated services, sales of the Surface tablet and advertising revenue from Bing.

Besides Bing, a perennial loser lagging behind Googles search engine, More Personal Computing saw great success in the fourth quarter. People quarantined at home sought escape in video games, sending gaming revenue up 64% and Xbox content and services revenue up 65%; meanwhile, stay-at-home orders also encouraged consumers to snag a Surface tablet for remote work and education, resulting in a 28% increase in Surface revenue.

Combined with Windows, these diverse businesses propelled revenue in the More Personal Computing segment up 14% in a quarter when the vast majority of companies around the world could only dream of such returns. But the big growth driver at Microsoft right now is the cloud.

The second major pro to buying Microsoft stock is its growing focus on the cloud. The company does this in two ways: First, it offers its suite of productivity applications, Microsoft Office, as a cloud-based software as a service offering. Instead of earning a one-time cut when someone buys a Windows- and Office-equipped computer, consumers now pay Microsoft $99.99 a year to use Office across all devices.

The second way Microsoft is cashing in on the cloud is with its cloud computing offering Azure. Its the second-largest player in the rapidly growing field, trailing only Amazon and its Amazon Web Services. In the fourth quarter, Microsoft Azure revenue grew by 47% year over year, fueling the 19% increase in server products and cloud services revenue year over year.

But the strength of Azure and Microsofts cloud services was enough to propel the Intelligent Cloud segment to 17% revenue growth this past quarter, and that growth will likely only continue thanks to key contracts like Microsofts recent $10 billion deal with the U.S. Department of Defense.

The cloud, personal computing and Microsofts final segment, Productivity and Business Processes, all enjoyed strong revenue growth in the fourth quarter of fiscal 2020 and combined to push Microsofts fourth-quarter revenue up 13% year over year. As for the fiscal year itself, Microsofts revenue increased by 14% year over year, while earnings per share increased by 14%, too.

MSFT shareholders who have spent 2020 watching their portfolios take a roller coaster ride must be relieved that their investments include a company as stable as Microsoft. This brings up the final pro for investing in the house that Gates built: stability.

[READ: 15 of the Best Dividend Stocks to Buy for 2020.]

The risk you take on by investing in Microsoft is fairly low for long-term investors. Not only is Microsoft notably absent from the U.S. governments looming antitrust investigations into Big Tech peers Facebook, Alphabet and Amazon, but Microsoft is one of just two U.S. companies that all major credit rating agencies actually consider to be a lower default risk than the federal government.

Thats right: Microsoft, along with Johnson & Johnson ( JNJ), is more likely to pay back your loan than Uncle Sam. Its hard to be much more financially secure than that.

Cons of Buying Microsoft Stock

The cons to buying Microsoft stock? Those are a bit harder to find.

The most glaring risk might seem trite, but in simple terms, its that MSFT stock may be too high right now. By traditional metrics like the price-earnings ratio (PE) and price-earnings to growth ratio (PEG), Microsoft is trading at richer valuations than the S&P 500.

Theres nothing wrong with that on its face. Most growth stocks trade for higher multiples than the market at large, for the rational reason that earnings are expected to grow more quickly than the wider market.

The question, however, is whether a trillion-dollar company like Microsoft can still be expected to grow at a quick enough rate to justify its PE of 35. Back in the 80s and 90s, it wasnt unusual for earnings to double every two years or so, and its much easier to go from numbers like $100 million to $200 million than $1 trillion to $2 trillion. Theres only so much money, and so much growth, in the world especially considering the FAANG (Facebook, Apple, Amazon, Netflix, Google) stocks that are constantly vying for Microsofts business.

Speaking of FAANG stocks, theres another potential risk to keep abreast of: If a competitor develops a breakthrough in something like quantum computing, artificial intelligence, smart home devices or entertainment where Microsoft shouldve been competing more aggressively, thats a missed opportunity. But Nadella is far less likely to miss those massive paradigm shifts than the less technologically sophisticated Ballmer.

That said, Microsoft faces stiff competition in nearly every industry in which it dabbles. Surface sales may increase, but its doubtful theyll ever eclipse the iPad. Google is unlikely to lose out to Bing anytime soon. Azure is steadily gaining ground, but Amazon still remains the market leader. There are always new competitors ready to take on Microsofts dominance Slack ( WORK) is challenging Microsoft Teams, while the new Xbox Series X will face off against Sonys ( SNE) new PlayStation 5 this holiday season.

The key to Microsofts ongoing success remains Windows and the Office suite of products. That was true in the 1990s, and it is still true in 2020. As long as Microsoft remains dominant in those markets, it will be a viable company with a bright future ahead but investors should always be wary of new competitors lurking just over the horizon.

The Bottom Line on Microsoft Stock

The fact that the biggest risks associated with Microsoft stock are mostly just the usual risks associated with buying any stock is a remarkable statement.

For a company of its size to not have extreme legal or antitrust woes or hardcore competition threatening its bread and butter is remarkable. The fact that its financial security is considered safer than U.S. bonds is almost without parallel.

Microsoft has a great moat in an industry that will almost certainly still be around a decade from now; on top of that, at the time of this writing, it pays a modest 0.96% dividend. Thats slightly more than the 10-year Treasury at 0.6% right now. So if you can sit on your hands with 10-year Treasurys, you might as well buy some Microsoft youll get the dividend, and likely some sizable capital gains unless something goes horribly wrong, or Nadella decides to channel his inner Ballmer.

When you look at the risk versus reward, Microsoft is a phenomenal stock to own.

Saturday History – The Albany Herald

Posted on July 23, 2020 by admin

Today is the 207th day of 2020 and the 36th day of summer.

In 1952, Puerto Rico became a self-governing U.S. commonwealth.

In 1978, Louise Joy Brown, the first baby to be conceived via in vitro fertilization, was born in Greater Manchester, England.

In 2005, two major unions, the Teamsters and the Service Employees International Union, withdrew from the AFL-CIO.

In 2010, the website WikiLeaks released the Afghan War Diary, containing more than 75,000 secret documents from the United States war in Afghanistan.

TODAYS BIRTHDAYS: Henry Knox (1750-1806), first U.S. secretary of war; Maxfield Parrish (1870-1966), painter/illustrator; Eric Hoffer (1902-1983), philosopher; Rosalind Franklin (1920-1958), biophysicist; Estelle Getty (1923-2008), actress; Walter Payton (1954-1999), football player; Iman (1955-), model/actress; Thurston Moore (1958-), musician; Matt LeBlanc (1967-), actor; Lauren Faust (1974-), animator.

TODAYS FACT: The Viking Orbiter 1 spacecraft, while searching for potential landing sites for the Viking 2 Lander, snapped the famous Face on Mars photo of the planets surface on this day in 1976.

TODAYS SPORTS: In 1976, American Edwin Moses ran in his first international track and field event at the Montreal Olympics the 400m hurdles and won a gold medal, with a record-setting time of 47.64 seconds.

TODAYS QUOTE: You really have to save yourself because the critic within you will eat you up. Its not the outside world its your interior life, that critic within you, that you have to silence. Iman

TODAYS NUMBER: 4.9 million approximate combined membership of the Teamsters and the Service Employees International unions in 2019.

TODAYS MOON: Between new moon (July 20) and first quarter moon (July 27).

Now, more than ever, the world needs trustworthy reportingbut good journalism isnt free.Please support us by subscribing or making a contribution today.

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Saturday History - The Albany Herald

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

Posted on July 23, 2020 by admin

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

Adversarial Machine Learning and the CFAA – Security Boulevard

Posted on July 23, 2020 by admin

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

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

Posted on July 23, 2020 by admin

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)

Trusted Business InsightsShelly ArnoldMedia & Marketing ExecutiveEmail Me For Any ClarificationsConnect on LinkedInClick to follow Trusted Business Insights LinkedIn for Market Data and Updates.US: +1 646 568 9797UK: +44 330 808 0580

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

Some Bitcoin Traders Turn Cautiously Bearish Why $9.5K Is a Problem – Cointelegraph

Posted on July 23, 2020 by admin

The price of Bitcoin (BTC) surged from $9,160 to $9,584 within the last 48 hours. But despite the 4.6% gain, some traders are turning short-term bearish on the top cryptocurrency.

According to several technical analysts, the market structure of Bitcoin remains slightly bearish. At higher time frames, $9,500 could still technically be a lower high. The term lower high is used when the recent peak of BTC is lower than previous highs.

The daily chart of Bitcoin. Source: TradingView.com

On June 3, June 10, June 22, and July 22, BTC hit $10,473, $10,180, $9,794 and $9,584, respectively. Each peak is lower than previous highs, making it a lower high formation.

Some traders are bearish on Bitcoin for two major reasons, namely a lower high structure and declining volume.

Crypto trader Zoran Kole, for example, said that a bearish market structure at a higher time frame remains intact. On the daily chart, four consecutive lower highs indicate a potentially weak consolidation phase. He wrote:

HTF Bearish MS remains intact. One shouldn't use a potential LH as invalidation for a swing position unless that LH is confirmed with a LL. Patiently waiting for the 95xx sweep to compound. Looking to sell 9530-9580. Clear invalidation above 97/98 (break in MS).

A potential lower high at a higher time frame for Bitcoin. Source: Zoran Kole

Meanwhile, a pseudonymous trader known as Crypto ISO suggested that the market could see a pullback if BTC hit a lower high. For it to confirm, BTC would have to break down from $9,500. The trader said:

Would be tough for a lot of people if this is the lower high. Those that understand MS will get this.

Pseudonymous trader DonAlt said that for a short-term bearish trend to confirm, BTC ideally needs to drop below $9,300. If BTC stays above $9,500, the popular trader said short-term market bias could weaken.

He said:

Close above that red line today ($9300) and I might reconsider my short term bearish bias (mid-term bear bias remains). Close below and I'll consider shorting more aggressively targeting the green line ($8500) first and green area second (~$7000).

Apart from technicals and market structures, there are more fundamental factors supporting a bullish case for Bitcoin.

For instance, the hash rate of BTC remains resilient, leaving the mining ecosystem healthy. Lower selling pressure from miners, combined with declining exchange inflows, suggests BTC could see an uptrend.

Data from Binance Futures show that the majority of top traders on the platform remain majority long on Bitcoin. But over 50% of traders are short on large-market cap alternative cryptocurrencies like Ether (ETH) and XRP.

Raoul Pal, the CEO of Global Macro Investor, said on July 23 that Bitcoin could outperform gold, which has been on a strong rally in recent weeks. He stated:

The other bet is that bitcoin will likely beat gold too. The bitcoin/gold cross looks powerful but has yet to break out.

Whether the short-term bearish market structure could cause a near-term pullback, or bullish fundamental factors would offset the risk, remains to be seen.

Original post:
Some Bitcoin Traders Turn Cautiously Bearish Why $9.5K Is a Problem - Cointelegraph

Why BitMEX Just Invested In This South African Bitcoin Exchange – Forbes

Posted on July 23, 2020 by admin

The parent company of BitMEX invested in South Africa's largest Bitcoin exchange.

100x Ventures, the venture arm of the parent company of BitMEX, invested in South Africa's largest Bitcoin exchange, VALR.

Two factors likely propelled 100x Ventures to invest in the exchange. First, it positions the company for additional growth in overseas markets. Second, it allows the firm to meet the demand for regional exchanges and trading services.

The Series A funding round led by 100x Ventures raised $3.4 million for the exchange. VALR says it has 40,000 users and has processed 13,000 BTC in the past month.

Reasons the Investment Could Have Been Compelling For the BitMEX Parent Company

100x Venture's investment in VALR directly positions itself to gain exposure to a developing crypto market in South Africa.

BitMEX has remained a dominant force in the futures market for several years. But, regional investments allow BitMEX and 100x Group to diversify its business further.

According to data from Skew.com, BitMEX remains as the most dominant Bitcoin futures exchange in the global market. It has an open interest of $1.02 billion, more than double its competitors, like Binance and Bybit.

The open interest of Bitcoin futures exchanges.

Another reason for the investment could have been the rapidly expanding regional peer-to-peer Bitcoin markets. In many countries outside of major cryptocurrency markets, there is a shortage of well-regulated and transparent Bitcoin exchanges.

Consequently, the volume of peer-to-peer exchanges that allow users to trade directly with one another began to surge.

Technology researcher Kevin Rooke found that peer-to-peer Bitcoin volumes recently hit record highs in India, Ghana, the Philippines, and Mexico.

Peer-to-peer trading is typically an alternative to exchanges in regions or markets that lack a proper exchange infrastructure.

Given the evident increase in demand for regional exchanges, the investment of 100x Venture fills a gap between major and relatively small crypto markets.

Arthur Hayes, the CEO and co-founder of 100x Group, said:

"South Africa has an incredibly exciting and fast-growing cryptocurrency ecosystem, and we believe VALR is well-placed to capitalize on future growth of bitcoin trading. In VALR we're backing not only a successful early-stage business, but a management team with the ability to scale operations significantly."

The Timing of the Investment Coincides With Positive Market Sentiment

After the highly-anticipated block reward halving in May, the hash rate of the Bitcoin blockchain network soared to record highs.

It shows that the mining sector is healthy, despite the abrupt decline in a large portion of their revenues.

A stable mining industry could cause selling pressure on the cryptocurrency exchange market to gradually decline in the medium-term.

The investment comes during a period when the sentiment is largely positive. The price of Bitcoin has tended to rally following every block reward halving, and some investors foresee a prolonged uptrend.

Jason Williams, the co-founder and partner at Morgan Creek Digital,saidhe expects a new all-time high for Bitcoin in 2020.

A confluence of the growing demand for regional exchanges, the need to diversify 100x Group's portfolio, and positive market sentiment appear to have led to the investment in VALR.

Will Trump ban TikTok in the USA? – Vox.com

Posted on July 23, 2020 by admin

TikTok was never supposed to be political. When it launched in the US in 2018, the video app was marketed as a fun place to discover goofy content and experiment with its sophisticated editing software and vast music library. Yet nearly two years and 165 million nationwide downloads later, TikTok has been a platform for teachers strikes, QAnon conspiracy theories, Black Lives Matter protests, and a teen-led campaign to sabotage a Trump rally in Tulsa, Oklahoma. The TikTok algorithm is perfectly suited to spread political content faster and to a wider audience than any social media app in history, whether the company wants to admit it or not.

Now TikTok is proving itself to be political in a much broader way, one that challenges the very existence of the app. White House officials are talking seriously about attempting to ban it (how the government would choose to do so is less clear) in the wake of rising tensions with China, where TikToks parent company ByteDance is based.

There are two major factors at play when we talk about the risks TikToks ownership could potentially pose: data privacy and censorship. While the former is potentially easier to understand (the Equifax hack, where members of the Chinese military were charged with stealing the personal information of 145 million Americans, is perhaps the most famous example), the latter, which includes how TikTok instructs its moderators and changes its algorithm, could have more existential and more difficult-to-predict consequences for the US at large.

Will a ban actually happen? President Trumps chief of staff, Mark Meadows, said in July that a decision could come in weeks, not months. But the conversation is a lot more complicated than Is China stealing our data? although thats likely how the Trump White House would prefer to frame it. TikTok has become a straw man for fears over a serious competitor to Silicon Valley: If a generation of kids is synonymous with an app owned by China, what does that mean for Americas role in global technology?

Experts in cybersecurity and Chinese tech make it clear that the issue is not black and white, and that serious concerns about national security are likely rooted not in xenophobia but in the fact that the Communist Party of China (CCP) under President Xi Jinping has a track record of surveillance, censorship, and data theft. There are also those who warn that the US banning TikTok and other Chinese-owned apps could set a dangerous precedent for a less free and open internet ironically, the sort of internet modeled after that of China.

The governments interest in TikToks ties to China and its communist leadership stems from last fall, when Sens. Marco Rubio (R-FL), Chuck Schumer (D-NY), and Tom Cotton (R-AR) called for an investigation into the company. Their statements came after reports from the Guardian and the Washington Post revealed that TikTok had at one point instructed its moderators to censor videos considered sensitive by the Chinese government.

By November, the Committee on Foreign Investment in the United States (CFIUS), which investigates the potential national security implications of foreign acquisitions of US companies, announced that it would be reviewing ByteDances acquisition of Musical.ly, the app that would become TikTok. Meanwhile, TikTok has been steadfast in its claim that it does not send US user data to China and does not remove content sensitive to its government and would not if it were asked. Two Chinese intelligence laws from 2014 and 2017, however, require companies to assist with any government investigation and hand over all relevant data without refusal.

In a statement to Vox, a TikTok spokesperson wrote:

Protecting the privacy of our users data is of the utmost importance to TikTok. Theres a lot of misinformation about TikTok right now. The reality is that the TikTok app isnt even available in China. TikTok is led by an American CEO, with hundreds of employees and key leaders across safety, security, product, and public policy in the U.S. TikTok stores U.S. user data in Virginia, with backup in Singapore, and we work to minimize access across regions. We welcome conversations with lawmakers who want to understand our company. Were building a team here in Washington, D.C. so lawmakers and experts can come to us with questions or concerns. We know that actions speak louder than words, which is why were opening Transparency Centers in LA and DC so that lawmakers and invited experts can see for themselves how we moderate content and keep our users data secure.

In early July, Secretary of State Mike Pompeo told Fox News that the US was considering a TikTok ban after months of rising tensions with China and a ban of more than 50 Chinese apps including TikTok in India the week prior. Since then, TikTok users have been panicking over the potential loss of the internets greatest time waster; the Senate just advanced a bill to ban TikTok from all government devices. Facebook, too, is closing in: The company announced it will launch its copycat product, Instagram Reels, in the US in August.

Banning TikTok isnt as straightforward as it may sound in a country built upon the First Amendment, but there are several ways it could take place. The first is that CFIUS could force ByteDance to sell off TikTok to a US-owned company by determining it a national security risk (thats what happened to Grindr after it was sold to a Chinese company). Another is that it could put TikTok on whats called the entity list so that US companies like Apple and Google would be forced to remove it from their app stores. Adi Robertson at The Verge has a thorough examination of all these possibilities, but lets get to the real issue at play.

The case for banning TikTok, for many cybersecurity professionals, is relatively simple: The risk is simply too great, no matter how wonderful the content on the app may be. Kiersten Todt, managing director of the Cyber Readiness Institute, says that despite what TikTok claims, If the Chinese government wanted that data, they would be able to get that data.

While that may not scare the apps large user base of teenagers who are pretty sure the Chinese government doesnt care about their scrolling habits, Todt says its possible China could be building dossiers on high-profile individuals, including information like passwords, bank accounts, internet addresses, or geolocation, all of which could then be cross-referenced with even more personal data on other apps.

Ive been in the national security space for a couple of decades, and there is decades worth of evidence and data around Chinese interest, intent, and capability to hack the US, whether thats through intellectual property or through data theft, Todt says. The Chinese government hacked the broadest database of personnel in the US government. Theyre the only ones who have done that.

Todts other concern relates to Chinas role in the global tech wars at large. Artificial intelligence is only as good as the data that goes into it, and so if China continues to collect all of this data from populations around the world, its artificial intelligence has a lot more data input into it. How might it aggregate that data for the purposes of innovation, research and development and science? she asks. That can sound xenophobic, but it is a national security statement, just as we are cautious about Russia and Iran and North Korea for different reasons.

There are other arguments for banning TikTok, ones that relate to moderation and censorship. I find the data privacy issue to be a bit of a red herring, says Jordan Schneider, host of the ChinaTalk podcast and newsletter. The Chinese government has many likely more impactful ways of getting blackmail or corporate secrets or just general information about individual US nationals.

Instead, Schneider argues that the problem is the Chinese Communist Partys potential ability to influence conversation about politics on the app. People today are very concerned about the amount of power [Facebooks] Mark Zuckerberg has to value one type of speech over another or impacting elections by tweaking the algorithms and end up changing peoples opinions on certain things. So imagine if someone with the equivalent of Mark Zuckerbergs level of power over the US has no choice but to do what the CCP wants it to do? My sense is that is the case with ByteDance. He uses recent examples of Chinese disinformation campaigns on Twitter, Facebook, and YouTube around topics like the Hong Kong protests and Taiwanese independence.

I think theyve probably learned the lesson of 2016, which is that Russia can interfere in elections and basically get away with it, he says. What might that look like? For the average TikTok user, it wont really look like anything. You can just push certain videos more than others, and theres no open API to double-check these things, Schneider says. At the end of the day, the Chinese government clearly has the leverage to push ByteDance to do this sort of thing, and would honestly be dumb not to, because the prize is enormous, which is the ability to influence who the next president of the United States is.

It would be easy to leave it there, but Samm Sacks, a senior cybersecurity policy fellow at Yale Law Schools Paul Tsai China Center and New America who has testified before the Senate Judiciary Committee, warns against conflating Chinese tech companies with the CCP. There is much more of a push and pull in that relationship there, particularly around the security services access to private data, she says.

Plus, she argues that the incentive to censor content and steal user data is worth less than owning one of the worlds most important global tech companies. TikTok was intended to thrive and fly on its own overseas, and so its not necessarily in the Chinese government or ByteDances interest to set up the company to be secretly beholden to Beijing. Theres a commercial incentive at play that I think we have to take into account.

TikTok has, for many people in American politics and tech, become an existential threat that no amount of distancing itself from China building headquarters in the US and London, hiring a former Disney executive as its CEO will undermine. TikToks terms of use and black box algorithm are virtually identical to Facebooks policies, but its success has foreshadowed a potential end to Silicon Valleys dominance. Unspoken in many tech executives dismissal of TikTok is protectionism and, arguably, xenophobia.

Should the US government ban TikTok, Sacks says, it would be an important step toward the US government controlling the way that Americans use the internet, which is ironically a step toward Beijings own cyber-sovereignty, the very thing weve been railing against for years.

It also would likely be against the USs commercial interests. It offers a blueprint for others around the world to think, Maybe we dont trust the way that Silicon Valley companies are handling our data, so lets just ban them, too, she says. Were already starting to see the rise of digital sovereignty in Europe and in India in these really important markets, and when we think about the so-called tech competition with China, particularly with artificial intelligence and machine learning, what is it thats going to give US companies an edge? Its access to large international data sets. If we are increasingly closed out of markets around the world and access to that data because weve helped create a blueprint for how to do it with China, I could see those same tools turned around on us.

Instead, Sacks has called for a comprehensive federal data privacy law that would be applied to all platforms, not just Chinese-owned ones, that would create standards for better data security, algorithmic transparency, and better management of online content. All of the things that I think were using is China as a foil and saying, That company is a threat, lets stamp them out, [could be dealt with by] developing our own vision for how we want to govern the internet in a more democratic, secure way, she says.

China aside, a TikTok ban would have serious effects on American youth culture, where hundreds of teenagers have now built massive followings and spread important political messaging on an app that allowed them to reach huge audiences. Its changed not only the experience of being online but the experience of being a young person.

TikTok has serious flaws conspiracy theories in particular, some related to QAnon, Pizzagate, and the coronavirus, have thrived unchecked on the app but theres still no evidence that the Chinese government has anything to do with any of those. Would setting a precedent against any one Chinese-owned tech company solve the immediate issues that affect American social media users, namely misinformation, content moderation, and transparency? Or would it allow Silicon Valley companies like Facebook to continue to mimic competitors software and grow ever larger and more powerful? Its now in the hands of the government to decide.

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Concern and Alarm? XRP Investors Debate Future of Top Cryptocurrency and Payments Startup Ripple – The Daily Hodl

Posted on July 23, 2020 by admin

XRP investors are voicing their concerns about the top crypto asset.

After a brief price surge to about $0.344 in mid-February, Ripples native cryptocurrency has since dropped back to Earth. The fourth-ranked digital asset by market capitalization has seen gains of about 5% in 2020, trading around $0.20 at the time of writing.

The modest increase pales in comparison to Bitcoin, which has seen gains of about 32%, and Ethereum, which has surged 101% on the year.

With the price of XRP seemingly in purgatory, a Redditor is asking whether anyone is concerned and alarmed with the constant pivoting and lack of clarity from Ripple. The San Francisco payments startup owns more than half of the total supply of XRP and is pushing for adoption of the cryptocurrency on multiple fronts.

Last year the deal with MoneyGram per [Ripple CEO Brad Garlinghouse] was a game changer and bigger deal than Libra, this year. it was just a pilot. Years ago we per Brad focused on cross border payments now its decentralized exchanges with a five-year goal? Dozens of banks will hold XRP? 300 plus customers in 40 countries? Customers of what? What are they paying for? [Zero] clarification? To me Ripple seems aimless, having a great product but trying to find something its good for.

Some Redditors countered by pinning the blame on the U.S. Securities and Exchange Commission for not providing clearer regulations. They say Ripple is hamstrung by the ongoing unregistered-security lawsuits.

But the original poster and others are skeptical that regulatory factors are keeping XRPs success at bay, arguing that Ripples XRP-powered cross-border payments product, On-Demand Liquidity, still hasnt taken off in regions where there is regulatory clarity.

Banks and financial institutions in countries that do have regulatory clarity still arent rushing to get on board with ODL. Its becoming more clear that no one really is interested in ODL. Time to start throwing this shit at a different walk to see if it sticks.

More optimistic Redditors are pointing to Ripples partnership with the financial titan SBI Holdings as a sign of whats to come. The companies are gearing up to launch ODL in Asia later this year.

See the original post here:
Concern and Alarm? XRP Investors Debate Future of Top Cryptocurrency and Payments Startup Ripple - The Daily Hodl

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