Developing new drugs to help cure diseases is a challenge some of the world's sharpest minds are focused on. It's also something that's enormously expensive and time-consuming -- with new drugs sometimes taking years, and costing hundreds of millions of dollars, to bring to market. That's where a United Kingdom-based company called BenevolentAI wants to help -- and it's using the world's most advanced deep learning supercomputer to do so. The first company in Europe to use Nvidia's state-of-the-art DGX-1 computer, the idea is to utilize cutting-edge chemical modeling algorithms to come up with ways to treat serious diseases faster than was previously thought possible. "We're taking giant corpuses of data, hundreds of millions of documents and structured data sources, and using it to discover relationships between chemicals, diseases and information about the body," Derek Wise, vice president of engineering at BenevolentAI, told Digital Trends.

LinkedIn wants to become more useful to workers by adding personalized news feeds, helpful messaging "bots" and recommendations for online training courses, as the professional networking service strives to be more than just a tool for job-hunting. The new services will arrive just as LinkedIn itself gains a new boss -- Microsoft -- which is paying 26 billion to acquire the Silicon Valley company later this year. LinkedIn said the new features, which it showed off to reporters Thursday, were in the works before the Microsoft takeover was announced in June. But LinkedIn CEO Jeff Weiner said his company hopes to incorporate some of Microsoft's technology as it builds more things like conversational "chat bots," or software that can carry on limited conversations, answer questions and perform tasks like making reservations. Chat bots are a hot new feature in the consumer tech world, where companies like Facebook, Apple and Google are already racing to offer useful services based on artificial intelligence. As a first step, LinkedIn says it will soon introduce a bot that could help someone schedule a meeting with another LinkedIn user, by comparing calendars and suggesting a convenient time and meeting place.

If you just consider how sports data was presented or what was available five years ago, the amount of data is simply exploding,

Developing a computer that can be as decisive and intelligent as humans is on IBM's mind, and it's making progress toward achieving that goal. IBM's computer chip called TrueNorth is designed to emulate the functions of a human brain. The company is now running tests and benchmarking TrueNorth to demonstrate how fast and power efficient the chips can be compared to today's computers. The results of the head-to-head contest are impressive. IBM says TrueNorth can engage in deep learning and make decisions based on associations and probabilities, much like human brains.

Apple released its new Mac operating system, Sierra, earlier this week, which is available to all Mac users as a free download through Apple's Mac App Store. The update notably adds Siri to the Mac and also many of the features from iOS 10, including enriched messages. That's a new name change from Apple, but loyal Mac users will find everything pretty familiar. Sierra is a big update, and there are lots of little things that you may not necessarily find on your own, hiding just below the surface. Here are a handful to help you get the most of the update.

Apple is on a machine learning company buying spree. After buying Perceptio at the end of 2015 and Turi just a few months ago, Apple has now acquired an India/US-based machine learning team, Tuplejump. We'd been hearing rumors of another acquisition in this space by Apple for some time. Apple buys smaller technology companies from time to time, and we generally do not discuss our purpose or plans. That's okay -- as with most machine learning companies, they're not exactly a household name (unless you've got a data scientist in your house, I guess.)

When it comes to diseases like cancer, much can be read into the language we use. For instance, there's a great deal of debate about whether military terminology like "battling" cancer are useful, or suggest that the disease is a fight that only the strongest can win. Likewise, Silicon Valley's tech companies in recent years have unveiled moonshot initiatives they claim will someday "cure" diseases like cancer. When announcing a 3 billion investment into life sciences research, Mark Zuckerberg described his goal to cure the disease within his daughter's lifetime. We've been using such terminology for decades, but is it helpful?

Apple is continuing to add to its team of machine learning experts in Cupertino. TechCrunch reports that Apple has acquired Tuplejump, which describes itself as a service that "presents all your data in a familiar format" on their now-removed website. Apple buys smaller technology companies from time to time, and we generally do not discuss our purpose or plans. The report notes that Tuplejump is based in part in India as well as the United States and doesn't disclose the terms of the acquisition. We're hearing that Apple was particularly interested in "FiloDB", an opensource project that Tuplejump was building to efficiently apply machine learning concepts and analytics to massive amounts of complex data right as it streamed in.

Last summer, I was at a conference having lunch with Hal Daume III when we got to talking about how "Bayesian" can be a funny and ambiguous term. It seems like the definition should be straightforward: "following the work of English mathematician Rev. Thomas Bayes," perhaps, or even "uses Bayes' theorem." But many methods bearing the reverend's name or using his theorem aren't even considered "Bayesian" by his most religious followers. Why is it that Bayesian networks, for example, aren't considered… y'know… Bayesian? As I've read more outside the fields of machine learning and natural language processing -- from psychometrics and environmental biology to hackers who dabble in data science -- I've noticed three broad uses of the term "Bayesian."

It's certainly an exciting time be involved in Natural Language Processing (NLP), not only for those of us who are involved in the development and cutting-edge research that is powering its growth, but also for the multitude of organizations and innovators out there who are finding more and more ways to take advantage of it to gain a competitive edge within their respective industries. With the global NLP market expected to grow to a value of 16 billion by 2021, it's no surprise to see the tech giants of the world investing heavily and competing for a piece of the pie. More than 30 private companies working to advance artificial intelligence technologies have been acquired in the last 5 years by corporate giants competing in the space, including Google, Yahoo, Intel, Apple and Salesforce. It's not all about the big boys, however, as NLP, text analysis and text mining technologies are becoming more and more accessible to smaller organizations, innovative startups and even hobbyist programmers. NLP is helping organizations make sense of vast amounts of unstructured data, at scale, giving them a level of insight and analysis that they could have only dreamed about even just a couple of years ago.