Artificial Intelligence Meet Google’s New Open Source Machine Learning Tool, TensorFlow Published 3 years ago on November 9, 2015 By P. H. Madore The Money Makers Club now has 6 of 15 available seats. Learn more here! Machine learning is the nerdiest side of artificial intelligence. It’s the part you don’t see as much in the science fiction movies or read about as much in the tech press. However, machine learning is critical to the actual development of artificial intelligence, and it has been increasingly used in large scale projects in recent years. Most notably, machine learning is used in products like Google Photos, where its implementation centers largely around the identification of objects. But now Google is making machine learning much easier to grasp for the wider geek community who may be interested. Google’s new offering, TensorFlow, can scale from a smartphone up to a massive data center cluster. However, the current publicly available release actually can only run on one machine, but the design is there for major scaling. TensorFlow is open source, using the Apache license, which means other major companies will be able to fork their own versions. Smaller outfits can also make use of this ability, of course, shedding parts that may be unnecessary and building in new modules that may be required. The TensorFlow website features two introductions to the classic machine learning problem, a MNIST data set. The beginner level introduction calls MNIST, “MNIST is a simple computer vision dataset. It consists of images of handwritten digits.” Machine learning is still very much a nascent technology. Many projects who employ it still have to use human labor to verify the results. This can be witnessed most any day of the month on Amazon’s Mechanical Turk website, which is a market place for people to do small tasks for small rewards. Firms routinely use the labor via Mechanical Turk to verify machine learning data results, or sometimes in place of machine learning, even. TensorFlow is written in both Python and C++. Likely enough, one of the earliest forks of the project will be into Google’s own Go language, which is built on similar principals as Python. However, using Python makes at least a good portion of the program widely accessible to the development community, among which Python enjoys serious popularity. Python is considered a “high level” language, meaning that it takes less work for the human coder to communicate to the computer what he wants done, whereas a “closer to the metal” language means the coder has to be more instructive to the code in order to achieve similar results. Both types of languages are important in computer programming, and always will be. Without underlying “close to the metal” libraries for Python to interact with, it wouldn’t be nearly as efficient. The point is that by using Python, Google is making the software more attractive to more developers around the globe. Google may have a new business opportunity on its hands, at any rate. If it could make machine learning and its usage as easy to understand and manipulate as it has with AdSense and similar business-oriented software, it could see a new revenue stream by handling the machine learning of other outfits, delivering more bang for the buck and less overhead for smaller fees. Images from Shutterstock and Google. Important: Never invest (trade with) money you can't afford to comfortably lose. Always do your own research and due diligence before placing a trade. Read our Terms & Conditions here. Trade recommendations and analysis are written by our analysts which might have different opinions. Read my 6 Golden Steps to Financial Freedom here. Best regards, Jonas Borchgrevink. Rate this post: Important for improving the service. Please add a comment in the comment field below explaining what you rated and why you gave it that rate. Failed Trade Recommendations should not be rated as that is considered a failure either way. (0 votes, average: 0.00 out of 5)You need to be a registered member to rate this. Loading... P. H. Madore 5 stars on average, based on 2 rated postsP. H. Madore has covered the cryptocurrency beat over the course of hundreds of articles for Hacked's sister site, CryptoCoinsNews, as well as some of her competitors. He is a major contributing developer to the Woodcoin project, and has made technical contributions on a number of other cryptocurrency projects. In spare time, he recently began a more personalized, weekly newsletter at http://ico.phm.link Follow @HackedCom Feedback or Requests? Related Topics:Googlemachine learningTensorFlow Up Next Is the NSA Using Zero-Day Exploits before Reporting Them? Don't Miss Video: World’s First 3D Printed Jet-Powered UAV Takes Flight You may like Pre-Market: Stocks Grind Higher as Google Beats, Turkish Lira Tumbles The Internet of Shopping: Blockchain Solutions to Consumer-Retail Challenges ICO Analysis: Productivist FAANG Stocks are Bleeding after Google’s Quarterly Results ICO Analysis: Decentralized Machine Learning (DML) Crypto Update: Selling Pressure Intensifies Again as Google Bans Crypto-Ads Click to comment You must be logged in to post a comment Login Leave a Reply Cancel replyYou must be logged in to post a comment. Artificial Intelligence YEXT: An Invisible Force In Artificial Intelligence Published 6 months ago on February 24, 2018 By James Waggoner The Money Makers Club now has 6 of 15 available seats. Learn more here! YEXT, Inc. (NYSE: YEXT) is one of those behind the scenes companies involved in Intelligence Search that plays an important role in Artificial Intelligence. What does that mean? Remember the Amazon commercial? “Eco, order a 12” Pizza with pepperoni from Stromboli’s and have it delivered”. Today the vast majority of online searches go through third-party sources such as data aggregators, governmental agencies and consumers. The net result of this third party sourcing has been to produce “best guess” data that can often miss or misstate the target data field. YEXT developed a better way to source critical digital knowledge. For example business clients use YEXT to update public facts about their brands. They are building their based on the rapid and ever changing nature of data. So far the YEXT Knowledge Network offers over 100 services to more than 110 corporate clients and has over $150 million in annual revenue. So could YEXT play a key role in AI, the next big thing? How YEXT Works Most of us are familiar with big time search engines like Google, Google Maps, Facebook, Instagram, Bing, Cortana, Apple Maps, Siri and Yelp. These pioneering companies are the major drivers in information search today. However, we also know, their accuracy is not exactly ideal. This is where YEXT steps in. Their knowledge engine platform lets business manage their digital knowledge in the cloud and sync it to over 100 services including the kingpins of search noted above. Intelligent Search is the structured information that a business wants to make publicly accessible. In food service it could be the address, phone number or menu details of a restaurant; in healthcare, the health insurances accepted by a physician or the precise drop-off point of the emergency room at a hospital campus; or in finance, the ATM locations, retail bank holiday hours or insurance agent biographies. Artificial Intelligence Offers a Potential $10 Billion Market Improving search results in general is nice but not very sexy. It doesn’t make you want to beg for more information. However, when you consider the role of Artificial Intelligence (AI) in our evermore data intense world, the importance of Intelligent Search and the opportunities for YEXT becomes a compelling story. The AI trend is already underway as YEXT is increasingly using the structured data on their platform to expand or add new integrations with vertically specialized applications, voice-based search and AI engines. Just Right For Big Data Applications YEXT customers use their platform to manage their digital knowledge covering over 17 million attributes and nearly one million locations. These customers include leading businesses in a diverse set of industries, such as healthcare and pharmaceuticals, retail, financial services, manufacturing and technology. Major customers include: AutoZone, Ben & Jerry’s, Best Buy, Citibank, Denny’s, Farmers Insurance Group, H&R Block, HCA, Infiniti, Marriott, Michael’s, McDonald’s, Rite Aid, Steward Health Care and others. The list is growing. Management believes the market for digital knowledge management is large and mostly untapped with over 100 million potential business locations and points of interest in the world equaling over $10 billion. Shooting For Acquisitions and Broad AI Penetration Founded in 2006 by serial entrepreneurs Howard Lerman (CEO) and Brian Distelburger, President these two are typical software guys whose vision appears much more broad based the their current focus with YEXT. Here is where the prospectus from their April 2017 IPO offers some mystery and excitement to the story. Unlike most rapid growth tech companies YEXT had no urgent need to go public. They generated almost $60 million in gross profit in 2016 before heavy marketing costs resulted in a loss of $26.5 million. Even so, they still ended the year with $20 million in cash. That’s a fair distance from being destitute. The company’s real need for the IPO was to establish a liquid public market for the stock. They raised about $123.5 million, all of which will go into the bank. The company is debt free and there are no insiders selling stock. Very interesting. Strong Financial Results For the latest reported nine months ended October 31, 2017 revenues grew 38% reaching $122 million. The good news is the gross profits reached a record 75% or $90 million. All of this was spent on sales and marketing to expand the business. When all the beans were counted, YEXT lost $50 million producing a $30 million negative cash flow. The balance sheet remains liquid with $120+ million in cash and securities. FYI: In spite of some top notch bankers underwriting its IPO and analysts from those same five firms covering the company, the stock has done almost nothing for investors. This $1.1 billion market cap was recently hanging out around $12 about the same as the IPO price. Featured image courtesy of Shutterstock. Important: Never invest (trade with) money you can't afford to comfortably lose. Always do your own research and due diligence before placing a trade. Read our Terms & Conditions here. Trade recommendations and analysis are written by our analysts which might have different opinions. Read my 6 Golden Steps to Financial Freedom here. Best regards, Jonas Borchgrevink. Rate this post: Important for improving the service. Please add a comment in the comment field below explaining what you rated and why you gave it that rate. Failed Trade Recommendations should not be rated as that is considered a failure either way. (2 votes, average: 4.50 out of 5)You need to be a registered member to rate this. Loading... James Waggoner 4.4 stars on average, based on 96 rated postsJames Waggoner is a veteran Wall Street analyst and hedge fund manager who has spent the past few years researching the fintech possibilities of cryptocurrencies. He has a special passion for writing about the future of crypto. Follow @HackedCom Feedback or Requests? Continue Reading Artificial Intelligence The End of Human Money Managers Published 7 months ago on January 21, 2018 By Fredrik Vold The Money Makers Club now has 6 of 15 available seats. Learn more here! Quantitative Easing by central banks around the world has led to dramatic changes in the money management industry over the past six years. Not only have we seen increasing regional differences, but stock picking has also become more difficult as the money injected into the markets by central banks has lifted pretty much everything, regardless of valuation and the future potential of the asset. Investors have become impatient and highly demanding as a result of years of low interest rates. Old mutual funds are being swapped out for new, better ones at a record pace as investors hunt for higher ROI. Passive income has become a trend, and ETF’s and automated investment strategies are getting more and more popular as a result. How do money managers attract capital? There are three main factors that determine how much capital a money manager is able to attract from investors: Track record Strategy Technology Changes in any of these factors can have a big impact on investors’ willingness to let the fund manager keep the money they have already invested with him, or receive new money. Technology has been a very important driver over the past few years. Data-driven, or quantitative funds are gaining an ever-increasing market share in the money management space. This is happening because more and more people are realizing the obvious benefits that this type of money management has to offer. Investors increasingly prefer the robustness, speed, and predictability that automated money management can provide. When it comes to robustness, we are referring to both the physical and psychological aspect of it. Humans vs. robots Humans are pretty much the opposite of “robust,” in the true sense of the word. Our emotional state on any given day can make us react to things in different ways than we otherwise would have done, potentially leading to critical mistakes for a trader. As humans, we may miss trading opportunities in the market because we came in late, took a day off, or simply didn’t pay attention at any given moment. Robots are obviously not affected by fatigue and lack of focus. For example, a robot can monitor the stock or cryptocurrency market and trade just like a human trader would do, with the only difference being that the former (arguably) does it better and never needs to rest. Thanks to the high computing power available today, robots can collect, verify, analyze, and react to opportunities long before a human will even understand that such opportunities exist. Data-driven approach to fund management is taking over A recent ranking by Institutional Investor Magazine revealed that out of the world’s 100 biggest hedge funds, five of the top six spots were held by data-driven funds. On first place was Ray Dalio’s Bridgewater Associates with $122.3 billion under management. In 2016, Bridgewater grew the amount of money under management by 17%. Renaissance Technologies, the company known for having hundreds of mathematicians, physicists, and coders on their payroll, came in fourth with $43 billion. Two Sigma, which is also well-known for using technologies like AI and machine learning, came in fifth with $39 billion under management. Their increase from the year before was 28%. According to Barclays, $500 billion are now invested in purely data-driven funds, while JP Morgan claims that data-driven trading strategies accounts for a whopping 90% of global trading volumes in stocks. The core objective of any money manager is always to follow the money. That’s why we are seeing a race right now by the big players in the industry to use words like “technology-driven,” “artificial intelligence,” and so on. Whether or not that is true is not always a concern for them. Money managers are destined to unemployment Those who are really in trouble because of this huge change are the money managers themselves. Most of them will likely lose their jobs over the next few years. There is simply very little need for their very expensive services anymore, as robots are able to do the same thing in a much cheaper and more consistent way. As legendary investors Jim Rogers predicted a few years ago, the stock brokers will become broke and the farmers are going to be driving Lamborghinis. Maybe there will finally be some truth to this. Featured image from Pixabay. Important: Never invest (trade with) money you can't afford to comfortably lose. Always do your own research and due diligence before placing a trade. Read our Terms & Conditions here. Trade recommendations and analysis are written by our analysts which might have different opinions. Read my 6 Golden Steps to Financial Freedom here. Best regards, Jonas Borchgrevink. Rate this post: Important for improving the service. Please add a comment in the comment field below explaining what you rated and why you gave it that rate. Failed Trade Recommendations should not be rated as that is considered a failure either way. (5 votes, average: 4.80 out of 5)You need to be a registered member to rate this. Loading... Fredrik Vold 4.3 stars on average, based on 37 rated postsFredrik Vold is an entrepreneur, financial writer, and technical analysis enthusiast. He has been working and traveling in Asia for several years, and is currently based out of Beijing, China. He closely follows stocks, forex and cryptocurrencies, and is always looking for the next great alternative investment opportunity. Follow @HackedCom Feedback or Requests? Continue Reading Artificial Intelligence Bitcoin Giant Bitmain Enters the High Stakes AI Race Published 12 months ago on August 27, 2017 By Lester Coleman The Money Makers Club now has 6 of 15 available seats. Learn more here! The Sophon, named for a fictional proton-sized supercomputer, could be the tool to train neural networks in data centers worldwide. It is the latest project being developed by Bitmain Technologies Ltd., the bitcoin mining giant that has carved out a dominant position in bitcoin mining. Such chips, called application-specific integrated circuits (ASICs), could unleash a new wave of distributed computing, according to Michael Bedford Taylor, a University of Washington professor who studies bitcoin mining and chips. Sophon is due to debut before the end of the year. Bitmain Has The Know-How Bitmain has the background to play a role in the expanding artificial intelligence industry. The company designs the silicon that goes in bitcoin mining equipment, assembles the machines and sells them worldwide, in addition to its own bitcoin mining operation and the ones that it manages for other mining pools. Bitmain’s founders are not averse to playing a spoiler role. Jihan Wu, the co-founder of Bitmain, supports the New York Agreement that seeks to double the bitcoin block size under the SegWit2X proposal, a move that some in the bitcoin community view as an attempt to give the miners control over bitcoin. Some also believe Wu was behind the recent bitcoin split known as bitcoin cash, which at least one of Bitmain’s miners supported, a contention that Wu has denied. Wu points out that he was among the supporters of Bitcoin Unlimited, an earlier bitcoin scaling proposal that did not get activated. Why Wu Supports Forks Wu nonetheless said splits should be allowed. He said a fork is inevitable since people in the bitcoin community do not agree on how to best scale bitcoin. Wu met Micree Zhan, Bitcoin’s co-founder, when Zhan was running DivaIP in 2010, a company that made a device that allowed a user to stream a TV show on a computer screen. In 2011, Wu needed a chip designer to build a mining operation and approached Zhan. Zhan first designed an ASIC to run SHA-256, the cryptographic calculation used in bitcoin, at maximum efficiency. It took him six months to finish the job. His first rig, Antminer S1, was ready in November 2013. Bitmain felt the sting of the 2014 Mt. Gox meltdown. But by 2015, bitcoin’s price bottomed out and later recovered. In the meantime, Bitmain introduced its Antminer S5. Bitmain now employs 600 people in Beijing. Also read: Bitmain clarifies its ‘bitcoin cash’ fork position Ready To Take On Google Bitmain has since developed a deep learning chip with improved efficiency. Users will be able to build their own models on the ASICs, enabling neural networks to deliver results at a faster pace. Google’s DeepMind unit used this technique to train its AlphaGo artificial intelligence. Bitmain plans to sell the chips to any company looking to train its own neural nets, including firms like Alibaba, Tencent and Baidu. Bitmain could build its own data centers with thousands of deep learning rigs, renting out the computation power to clients the way it does with bitcoin mines. Professor Taylor said companies like Bitmain that have excelled in bitcoin mining could take on the Googles and Nvidias since they have developed the skills to survive in an ultra-competitive and highly commoditized industry, and have the system level design expertise and the ability to reduce data center costs. Important: Never invest (trade with) money you can't afford to comfortably lose. Always do your own research and due diligence before placing a trade. Read our Terms & Conditions here. Trade recommendations and analysis are written by our analysts which might have different opinions. Read my 6 Golden Steps to Financial Freedom here. Best regards, Jonas Borchgrevink. Rate this post: Important for improving the service. Please add a comment in the comment field below explaining what you rated and why you gave it that rate. Failed Trade Recommendations should not be rated as that is considered a failure either way. (0 votes, average: 0.00 out of 5)You need to be a registered member to rate this. Loading... Lester Coleman 3.9 stars on average, based on 8 rated postsLester Coleman is a veteran business journalist based in the United States. He has covered the payments industry for several years and is available for writing assignments. Follow @HackedCom Feedback or Requests? 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