If you are looking for an answer to the question What is Artificial Intelligence? and you only have a minute, then here's the definition the Association for the Advancement of Artificial Intelligence offers on its home page: "the scientific understanding of the mechanisms underlying thought and intelligent behavior and their embodiment in machines."
However, if you are fortunate enough to have more than a minute, then please get ready to embark upon an exciting journey exploring AI (but beware, it could last a lifetime) …
COMPUTER BRAINS are tiny rectangles, becoming tinier with each new generation. Or so it used to be. These days Andrew Feldman, the boss of Cerebras, a startup, pulls a block of Plexiglas out of his backpack. Baked into it is a microprocessor the size of letter paper. "It's the world's biggest," he says proudly, rattling off its technical specs: 400,000 cores (sub-brains), 18 gigabytes of memory and 1.2trn transistors.
Why Partnership Strategy, not Technology, drives Digital Transformation? Known from the 17th century (Blaise Pascal invoked it in his famous wager, which is contained in his Pensées, published in 1670), the idea of expected value is that, when faced with a number of actions, each of which could give rise to more than one possible outcome with different probabilities, the rational procedure is to identify all possible outcomes, determine their values (positive or negative) and the probabilities that will result from each course of action, and multiply the two to give an "expected value", or the average expectation for an outcome; the action to be chosen should be the one that gives rise to the highest total expected value. Decision theory (or the theory of choice) is closely related to the field of game theory and is an interdisciplinary topic, studied by economists, statisticians, psychologists, biologists, political and other social scientists, philosophers, and computer scientists. The need for decision under uncertainty has never been stronger. Although the digital realm is evolving fast, the partnership strategical choice remains a human prerogative and a key driver of the digital ecosystem evolution.
Advances in technology can allow you to order food by voice or unlock your phone with your face, but those new capabilities could take a toll on the environment. Enhanced tech capabilities are being developed through the use of artificial-intelligence approaches like neural networks, which detect patterns in speech and images by training programs across countless data points. That process constantly crunches reams of information on power-hungry servers in data centers that use a substantial amount of energy to power, cool and monitor the servers. The result: Training a neural network can emit 17 times more carbon dioxide than an average American does in a year, and five times the lifetime emissions of an average car. Those are the findings of a recent paper by researchers at the University of Massachusetts, Amherst, which highlighted the substantial power generated by AI technologies.
There are more than 1.9 billion users logged in to YouTube every single month who watch over a billion hours of video every day. With this number of users, activity, and content, it makes sense for YouTube to take advantage of the power of artificial intelligence (AI) to help operations. Here are a few ways YouTube, owned by Google, uses artificial intelligence today. In the first quarter of this year, 8.3 million videos were removed from YouTube, and 76% were automatically identified and flagged by artificial intelligence classifiers. More than 70% of these were identified before there were any views by users.
The ability to forecast events at scale, given a set of variables, is something most companies would find useful. So Amazon is aiming to make prediction more accessible with a fully managed service called Forecast that uses AI and machine learning to deliver highly accurate forecasts. As Amazon explained in a press release, Forecast -- which is based on the same technology the Seattle company uses to anticipate demand for hundreds of millions of products every day -- can be used to build precise forecasts for virtually any business condition, including product demand and sales, infrastructure requirements, energy needs, and staffing levels. It automatically provisions the necessary cloud infrastructure and processes data, building custom AI models hosted on AWS without requiring an ounce of machine learning experience on the part of developers. Amazon says the API or a console allows the average person to build custom machine learning models in less than five clicks and achieve accuracy levels that would normally take months in as little as a few hours.
Tel Aviv is the city with the highest number of startups per capita in the world, according to the 2018 Global Startup Ecosystem report -- more than 6,000, of which 18 are unicorns. The city's tech cluster, dubbed Silicon Wadi, is home to more than 100 venture capital funds, plus hundreds of accelerators and co-working places. "Tel Aviv is transitioning from startup nation to scale-up nation," says Eyal Gura, co-founder of Zebra Medical Vision. Amit Gilon, an investor at Kaedan Capital VC fund, agrees – adding that Israel is not just about successful B2B companies anymore, such as Checkpoint, Nice and Amdocs, but also about "big B2C success stories like Playtika, Wix, Fiverr and others". Founded in 2015, Arbe has built a 4D ultra-high-resolution imaging radar for cars.
Machine learning has grown to have a significant impact on our daily lives: From Amazon's home assistant Alexa collecting and analyzing information to anticipate our needs, or Facebook suggesting who we should friend, to applications protecting us from credit card fraud and improving online shopping experiences. Organizations want their data to do the heavy lifting for them, driven by the desire to save on costs, improve consistency and streamline operations. While ML technologies were previously perceived as an excessive expenditure, today they are seen as an investment in the business' future and a competitive revenue driver. In order to stay competitive and successful, organizations have to invest in the right technologies and intelligently use the skills and data systems that they already have. The following three tips will help enterprises evaluate ML benefits and investments and make the most of the technology they already have.