Google has released the latest iteration of its machine learning system that figures out what's in an image and captions it, and it's better than ever. The company also made it open-source. Google has been working on the program since 2014, and now says the algorithm can describe a picture with 93.9 percent accuracy. The big question for the Google team, as they were working on this newest iteration that uses an Inception architecture, was whether the algorithm could do more than simply identify objects within images set before it. To really interpret and caption a photo, AI needs to understand not only what's in the picture but also how certain objects in the image interact with one another.

A subset of those scientists, engineers and programmers have a different goal: They're trying to use computer science to solve one of the most complex and deadly challenges humans face: Cancer. And, for the most part, they are doing so with algorithms and computers instead of test tubes and beakers. "We are trying to change the way research is done on a daily basis in biology," said Jasmin Fisher, a biologist by training who works in the programming principles and tools group in Microsoft's Cambridge, U.K., lab. One team of researchers is using machine learning and natural language processing to help the world's leading oncologists figure out the most effective, individualized cancer treatment for their patients, by providing an intuitive way to sort through all the research data available. Another is pairing machine learning with computer vision to give radiologists a more detailed understanding of how their patients' tumors are progressing.

According to technologists, the future of Fintech looks like this: we are all going to be paying for goods and services with our thumbs, robots will make sure our pensions don't decrease, AIs will manage hedge funds, online only banks will be available, we'll be able to get cash from our favorite merchant with our cell phones, credit and debit cards will become extinct, global money transfers will be automatic, market place lending will offer a peer-to-peer option, and it will be impossible to launder money. The incumbents and infrastructure that drive future finance will eventually be replaced by financial technology. However, I believe only those Fintech firms doing it for the right reasons will be successful. Is the new technology cheaper than the current norm? Does it provide a seamless, easy to use solution?

When it was first announced to a sceptical tech press months after a flop phone, the Echo was dismissed separately as a joke and a privacy nightmare. Now the latter may still prove to be the case - the Echo is always listening, and after it is awakened by saying "Alexa", it logs every sentence spoken to it (although Amazon says privacy is at the heart of the device and that users can delete queries that are stored) - but a joke it is clearly not. In fact many analysts now believe that Amazon has one hand on the future that comes after the smartphone. Alexa is not the only, or even the first, voice-activated virtual assistant – Apple, Google and Microsoft have had their own for years – but it is the first that consumers have truly embraced. While taking out a smartphone in public and speaking to it – as one must with Apple's Siri or Google's Assistant – is awkward, and often slower than simply using a touchscreen, talking to a device in the comfort of one's own home is decidedly less uncomfortable.

Transportation, health, safety and education are among the many sectors that will be impacted by advances in AI. However, AI is likely to completely transform the labour market and indeed society as a whole, and given that the way the general public react to AI will strongly influence the practical outcome, scientists and legislators will need to ensure that the economic and social benefits of artificial intelligence are shared widely, stress the report's authors. At the same time, AI also raises many questions, particularly ethical and social questions, such as the right to privacy. The medical sector will also feel the impact.

Artificial intelligence is not a threat to mankind. This is one of the conclusions of a 27-page report entitled'Artificial Intelligence and Life in 2030'. This, the first in a long series of planned reports, provides a summary of one year of research work, the first results from AI100, a project hosted by California-based Stanford University, whose purpose is to study the implications of artificial intelligence. Transportation, health, safety and education are among the many sectors that will be impacted by advances in AI. So what changes can be foreseen on the 2030 horizon?

Spark and pyspark have wonderful support for reliable distribution and parallelization of programs as well as support for many basic algebraic operations and machine learning algorithms. In this post we describe the motivation and means of performing name-by-name matching of two large datasets of company names using Spark. Our goal is to match two large sets of company names. We're looking at two long lists of company names, list A and list B and we aim to match companies from A to companies from B. In this example our goal is to match both GOOGLE INC. and Google, inc (from list A) to Google (from list B); and to match MEDIUM.COM to Medium Inc; and Amazon labs to Amazon, etc… OK, At first we thought we'd try the most simple and trivial solution, to see how well it works, if not for anything else, at the very least in order to establish a baseline for future attempts. The most simple thing to do is just case-insensitive string equation test.

Apple Inc. has acquired Indian machine-learning start-up Tuplejump Software Pvt Ltd as it seeks to expand its expertise in artificial intelligence. The iPhone maker bought the Hyderabad, India-based company in June, according to a person familiar with the deal who asked not to be identified. Tuplejump's software specializes in processing and analysing big sets of data quickly. The deal was reported earlier by TechCrunch. The purchase price wasn't disclosed.

Apple has just triggered a flurry of speculations as news was confirmed that it had acquired Tuplejump Software Pvt Ltd., a machine learning company operating in Hyderabad, India. The acquisition was reportedly completed last June and it rounded up the series of Apple startup purchases dabbling in artificial intelligence. Previously, the company acquired Turi Inc. for 200 million and it was followed by Emotient for a still unconfirmed amount. This last company is known in the industry for its employment of AI on facial expressions. These purchases are widely believed to be part of Apple's drive to improve its virtual assistant technology amid growing competition with Google.