By John Markoff For more than a decade, Silicon Valley's technology investors and entrepreneurs obsessed over social media and mobile apps that helped people do things like find new friends, fetch a ride home or crowdsource a review of a product or a movie. Now Silicon Valley has found its next shiny new thing. And it does not have a "Like" button. The new era in Silicon Valley centers on artificial intelligence (AI) and robots, a transformation that many believe will have a payoff on the scale of the personal computing industry or the commercial internet, two previous generations that spread computing globally. Computers have begun to speak, listen and see, as well as sprout legs, wings and wheels to move unfettered in the world.

Since the dawn of technology, there have been fears surrounding the future. What will these advances bring? How will they change our lives? What does this mean for my livelihood? In the twenty-first century these fears are the same.

Artificial intelligence is approximating human reasoning more and more closely all the time. Wide-scale adoption by business may be approaching, with important implications for how people live and work. AI is paving the way for new business models and raising questions about how people and machines can best work together. Now, thanks in part to cheaper and faster computing power, intelligent machines help doctors comb through troves of medical images to identify diseases early, allow manufacturers to predict when their machines will break (and fix them before that happens), and provide the "brains" behind increasingly autonomous vehicles. It's also playing a central role in the consumer market, powering the latest virtual assistant, for example, or the engine that matches Airbnb guests with the housing they want.

It's been a startling 17 quarters since IBM has produced any sales growth, when compared to the year-ago quarter. The slide was somewhat inevitable, as CEO Ginni Rometty took on the unenviable task of trying to modernize the company and started selling off commoditized businesses she saw as being marginal to IBM's future. Since taking the reins at IBM in 2012, she has shifted the company's focus to cloud services, as well as analytics, cybersecurity, and other more modern tech services that big companies might need--which she refers to as "strategic imperatives." She also committed to investing 1 billion in Watson, the company's cognitive computing software, as it aims to sell the artificial intelligence software platform to companies. Her strategies seems to be working: The "strategic imperatives" and cloud business lines grew 12% and 30% respectively in the second quarter, accounting for over half of the company's revenue for the period.

Google's DeepMind has moved on from playing Go to more serious matters - attempting to solve some of the world's biggest health problems. Projects include a tie-up with London Moorfields eye hospital, which will see it using one million eye scans to train its artificial intelligence system to diagnose potential sight issues, and development of an app to help doctors spot kidney disease. Google's entry on to the healthcare scene has been welcomed by some, notably doctors who are desperate to apply some cutting-edge technology to antiquated NHS systems. But less so by privacy groups and some patients, who have been surprised and concerned that their data - in some cases not anonymised - can be shared with the tech giant. So what does Google want with our health data and should we be worried?

A swarm intelligence similar to the one that predicted Oscar winners and Kentucky Derby finishers has come to nearly unanimous conclusions about the presidential potential of Hilary Clinton and Donald Trump. From social issues to trustworthiness and ethics, the swarm spoke loud and clear, expressing practically the same sentiment for both candidates -- extreme pessimism. The swarm consisted of 85 Democratic, Republican, or independent American voters who were asked to answer identical questions on Clinton and Trump through the swarm intelligence platform UNU. The speed at which they came to a conclusion helps calculate the percentage of "brainpower" for a particular swarm. Anywhere between 70 and 85 people participated in each round.

The following interview is one of many included in the report. As part of our ongoing series of interviews surveying the frontiers of machine intelligence, I recently interviewed Gurjeet Singh. Singh is CEO and co-founder of Ayasdi, a company that leverages machine intelligence software to automate and accelerate discovery of data insights. Author of numerous patents and publications in top mathematics and computer science journals, Singh has developed key mathematical and machine learning algorithms for topological data analysis. David Beyer: Let's get started by talking about your background and how you got to where you are today.

Even in the early days of bots, people were attempting to communicate with them as if they were human beings, Weizenbaum was attempting to create a bot that would learn from its interactions. IBM set up a competition between two of Jeopardy's most successful contestants and Watson; an intelligent natural language processor that uses machine learning technologies to answer questions. Google, Apple, Microsoft, and Autodesk are just a small sampling of the organizations working hard to build a bot that can interact with people using natural language and learn from those experiences. Watson is the blueprint; Slack, Facebook, Google, and Apple have examples of the interfaces humans will utilize.

Bots are currently all the rage, but they're not a new concept. Bots are only as useful as the services they are integrated with, and their purpose is essentially automation -- that is, creating and executing actions based upon a set of criteria. In order to know where bots are going, though, we need to understand where they've been. While no one knows exactly when bots started, they're widely thought to have gotten off the ground with ELIZA. The bot was built by Joseph Weizenbaum, an MIT professor, in 1964.

Over the past few months, the internet has been buzzing about bots. Microsoft, Facebook and other major players are opening up their A.I. platforms for developers, and these toolkits are an exciting and important step towards democratizing access to A.I. For the enterprise, however, the bot frenzy has accelerated a challenge that executives have been facing for the past couple of years. Many enterprise companies understand that they need an A.I. strategy and that the technology will be deployed throughout their business. Yet the challenge for them is where to actually begin. These companies understand that A.I. is a transformational integration for their business and that it will eventually touch everything from their customer service, their analytics and business intelligence, sales and CRM, and even internal knowledge management and HCM.