Only one of them is what we might think of as a tech guy, and it's that difference that means the other is likely to be right. Musk, a physicist by training, is a well-known AI radical who sees the technology as a threat to the human race because, in his view, it will inevitably outsmart us and start running the world without heeding our needs. "The biggest mistake I see AI researchers making is assuming that they're intelligent," he said during a debate in Shanghai on Thursday. "They're not, compared to AI." He likened humanity to a bootloader – a small piece of software needed to turn on a computer.

Around a year ago, amidst numerous desktop browsers, a LinkedIn notification blinked expectantly. The freshly minted digital magazine of Forbes unleashing its 30 Under 30's. Intrigued, I hastily found the social entrepreneurship section. This was definitely very special. But it would take more than half a year since connecting with Emily on LinkedIn, to find the guts to actually reach out and hear more…because in reality, other than the recent Sciences Po course I took on algorithmic governance, I had underestimated how much potential AI had within law enforcement and justice.

Big data has emerged as a lucrative by-product of the digital revolution, enabled by vast banks of information collated from the likes of smartphones, sensor networks, and cloud-based apps and databases. Deriving meaningful insights from this data can help cities figure out the real-time flow of people and traffic, for example, or enable life insurance providers to establish more accurate mortality rates. Extracting meaning from big data through analytics is estimated to be a $200 billion industry today, according to recent IDC numbers. It's against this backdrop that PredictHQ has come to fruition, offering a purpose-built data-aggregation platform that takes information from myriad sources related to events (public holidays, concerts, festivals, etc), meshes it with more "hard to find" data, adds a little machine learning to the mix, and sells it to third-party companies via an application programming interface (API). PredictHQ emerged from stealth last year with $10 million in funding and a host of big-name clients.

Facebook's hexapod, Daisy, learning to walk On the rooftop of the building that houses the Facebook AI Research (FAIR) lab in Mountain View, California, there is a bootcamp for robots where the sun beams down on Daisy, a hexapod who is learning how to walk on a dirt jogging path. Her foot has become stuck in mulch as she struggles to wrestle free. A team of Facebook AI researchers eagerly look on, watching to see what she will do next as she moves forward with the curiosity and experimentation of a toddler. One flight down, Daisy's counterpart Pluto, a red arm robot, is learning how to reach for an object in its playpen. Facebook is leading an effort to teach robots how to think for themselves and develop human-like intuition that will enable them to navigate unknown circumstances.

Jess, my wife, and I went shopping at Eaton Center in downtown Toronto recently. Jess was in a very good mood because she just came back from a Hackathon (a 3-day ideation and prototyping competition) her company organized. Jess is a Financial Advisor at a bank in Toronto. She was describing how fantastic (and unreal) all the Artificial Intelligence (AI) ideas and prototypes were as we stopped at a vendor booth to update my phone plan. A lady, named Joanne and probably in her early 20s, welcomed us and suggested a few good options; I signed up for one of her suggestions.

Cloning a voice typically requires collecting hours of recorded speech to build a dataset then using the dataset to train a new voice model. A new Github project introduces a remarkable Real-Time Voice Cloning Toolbox that enables anyone to clone a voice from as little as five seconds of sample audio. This Github repository was open sourced this June as an implementation of the paper Transfer Learning from Speaker Verification to Multispeaker Text-To-Speech Synthesis (SV2TTS) with a vocoder that works in real-time. The project was developed by Corentin Jemine, who got his Masters in Data Science at the University of Liège and works as a machine learning engineer at Resemble AI in Toronto. Users input a short voice sample and the model -- trained only during playback time -- can immediately deliver text-to-speech utterances in the style of the sampled voice.

Revolutions, it's been remarked, never go backward. Nor do they advance at a constant rate. Consider the immense transformation unleashed by data analytics. By now, it's clear the data revolution is changing businesses and industries in profound and unalterable ways. But the changes are neither uniform nor linear, and companies' data-analytics efforts are all over the map. McKinsey research suggests that the gap between leaders and laggards in adopting analytics, within and among industry sectors, is growing. Some companies are doing amazing things; some are still struggling with the basics; and some are feeling downright overwhelmed, with executives and members of the rank and file questioning the return on data initiatives. For leading and lagging companies alike, the emergence of data analytics as an omnipresent reality of modern organizational life means that a healthy data culture is becoming increasingly important. With that in mind, we've spent the past few months talking with analytics leaders at companies from a wide range of industries and geographies, drilling down on the organizing principles, motivations, and approaches that undergird their data efforts. We're struck by themes that recur over and again, including the benefits of data, and the risks; the skepticism from employees before they buy in, and the excitement once they do; the need for flexibility, and the insistence on common frameworks and tools. And, especially: the competitive advantage unleashed by a culture that brings data talent, tools, and decision making together. The experience of these leaders, and our own, suggests that you can't import data culture and you can't impose it. Most of all, you can't segregate it.

Wallops Island--a remote, marshy spit of land along the eastern shore of Virginia, near a famed national refuge for horses--is mostly known as a launch site for government and private rockets. But it also makes for a perfect, quiet spot to test a revolutionary weapons technology. If a fishing vessel had steamed past the area last October, the crew might have glimpsed half a dozen or so 35-foot-long inflatable boats darting through the shallows, and thought little of it. But if crew members had looked closer, they would have seen that no one was aboard: The engine throttle levers were shifting up and down as if controlled by ghosts. The boats were using high-tech gear to sense their surroundings, communicate with one another, and automatically position themselves so, in theory, .50-caliber

It's hard to ignore the anxieties and even polarization that one sees in so many places around the world today. The forces of globalization are reshaping our communities in tangible ways. Increasingly, more people voice concerns about their place in society and their cultural identity and heritage. We see this not only in the United States, but across Europe, in Asia and elsewhere. Technology has played a big role in accelerating globalization. While it's our business to advance technology, we also believe that technology should respect and even help protect the world's timeless values.

I am currently a Data Scientist in the AI team at Youplus, a startup based out of Bangalore and headquartered in New York City, which is building the world's first Video Opinion Search Engine. Within Youplus, I specialize in building a range of machine learning products that solve complex problems - some examples include product attribute extraction, opinion mining, coreference resolution etc. After graduating from Birla Institute of Technology and Science, Pilani (among top 10 premier engineering institutes in India) in Information Systems, I began my career as a Software Developer in Cisco systems where I worked in the platform team of ASR1k routers (mid-range enterprise edge routers). Later on, to satiate my quest for open source software development and desire to work on Big Data and Cloud Computing, I moved to Apigee (acquired by Google in 2016) where I worked in their R&D and Product Support teams. To further my interests and chart a career path in artificial intelligence(AI), I decided to pursue graduate studies in AI and moved to London to pursue the same at King's College London(ranked 31st in the world) in 2016.