It's no longer up for debate that AI is set to have a major impact on most businesses, if it isn't already--and any company that wants to stay ahead must figure out how to integrate the new technology into its structure. But how is a successful AI platform built? Perhaps best known as the guy who introduced Steve Jobs and Steve Wozniak, Bill Fernandez speaks out on Apple's founding magic, how love built the first Mac, and the interface of the future. In Mehanna's session, he explained how Facebook developed its own machine learning platform, and how Facebook employees are using it. In 2012, Mehanna said, Facebook's AI platform was "a snowball of complexity"--a system that slowed progress down significantly.

At any given moment, pharmaceutical firms have a massive library of compounds and no clue what to do with them. Tucked away in extensive collections of synthetic and theoretical drug banks lie hidden gems -- drugs to treat perhaps even the most devastating diseases -- but identifying them is a pain: testing can take years, even decades, and often researchers aren't even sure what they're looking for. What they need is a way to sort through the duds -- and now, it's looking like artificial intelligence can help. Scientists from Insilico Medicine, a bioinformatics firm, have figured out how to teach A.I. to predict the therapeutic use of new drugs before they're even tested. Publishing their work today in the journal Molecular Pharmaceutics, they discuss their A.I.'s training regimen, which involves taking in huge amounts of data from experiments on human cells using known drugs.

The R platform for statistical computing is perhaps the most popular and powerful platform for applied machine learning. The caret package in R has been called "R's competitive advantage". It makes the process of training, tuning and evaluating machine learning models in R consistent, easy and even fun. In this post you will discover the caret package in R, it's key features and where to go to learn more about it. Caret was built on a key philosophy in machine learning, that of the no free lunch theorem.

The smartphone boom upended Google's advertising profit engine and it took years for the Internet giant to adjust to the new mobile world. The next wave of computing will be an even bigger challenge. At Google's I/O developer conference this week near its Silicon Valley headquarters, the company unveiled new technology that will rely less and less on physical devices with screens to deliver information and services to consumers. Google hopes these advances will capture the human attention its business depends upon, and then it can figure out how to make money later, one executive said. Google Home will sit in living rooms sucking in voice-based queries and delivering verbal answers from an artificially intelligent "Google assistant."

The News and information ecosystem is in the midst of change -- again. Mobile-first consumption is on the rise, smart homes are becoming mainstream and connected cars will soon take over the roads of major cities around the world. Smart devices will require "smart content." It's only a matter of time before artificial intelligence (AI) becomes the backbone of the media industry of the future. Today, most people find information via search or social. And while these two channels are radically different in functionality, they have one thing in common -- any given article surfaced through these platforms is exactly the same for everyone in the world.

According to MarketandMarkets, the artificial intelligence (AI) market is estimated to grow from 419.7 million in 2014 to 5.05 billion by 2020, growing at a CAGR of 53.65% from 2015 to 2020. The Media and Advertising sector is expected to drive the growth of AI during this period. IBM, Microsoft, and Google are key players in the market, and now Salesforce is trying to make inroads into it. For the first quarter of fiscal 2017, Salesforce's revenue grew 27% over the year to 1.92 billion, above analyst estimate of 1.89 billion. Net income was 38.8 billion or 0.06 per share. Non GAAP EPS was 0.24, beating analyst forecast of 0.25.

Many times after my talks, people ask me whether algorithms could theoretically be better at making a diagnosis than doctors. With my doctor's cap on, I must defend the art of medicine. But as a medical futurist I need to tell my honest views. Making a diagnosis is an art. We humans are not engineering products, and therefore measuring a few parameters and tweaking a few knobs will not diagnose and cure our diseases.

Online abuse remains the big hairy monster in the room for platforms powered by user-generated content. Twitter especially has had some very sizable and public problems with problem users, taking flak in recent years for being the go-to social media conduit for orchestrated misogynistic campaigns, such as the #Gamergate example. Or more recently for being the training platform where trolls were able to teach Microsoft's ingenue AI chatbot Tay how to be racist and sexist double quick. Twitter knows it has a problem with users appropriating its platform to spread hate speech and/or harass others. Former CEO Dick Costolo conceded back in February 2015 that'we suck at dealing with abuse'.

Google has taken a big leap forward with the speed of its machine learning systems by creating its own custom chip that it's been using for over a year. The company was rumored to have been designing its own chip, based partly on job ads it posted in recent years. But until today it had kept the effort largely under wraps. It calls the chip a Tensor Processing Unit, or TPU, named after the TensorFlow software it uses for its machine learning programs. In a blog post, Google engineer Norm Jouppi refers to it as an accelerator chip, which means it speeds up a specific task.

If you're an experienced programmer interested in crunching data, this book will get you started with machine learning--a toolkit of algorithms that enables computers to train themselves to automate useful tasks. Authors Drew Conway and John Myles White help you understand machine learning and statistics tools through a series of hands-on case studies, instead of a traditional math-heavy presentation. Each chapter focuses on a specific problem in machine learning, such as classification, prediction, optimization, and recommendation. Using the R programming language, you'll learn how to analyze sample datasets and write simple machine learning algorithms. Machine Learning for Hackers is ideal for programmers from any background, including business, government, and academic research.