It's scary enough making a doctor's appointment to see if a strange mole could be cancerous so imagine you were in the middle of nowhere, couldn't take time off work or didn't have the money to see a doctor for a diagnosis. Now, thanks to a team at Stanford University you'll be able to whip out your phone, use an app and get a free, instant diagnosis. Universal access to low cost, let alone free health care, certainly in the US is a challenge, and that was on the teams minds when they set off to create an artificially intelligent (AI) algorithm that could diagnose skin cancer as well as a certified dermatologist. In one way this, along with other deep learning breakthroughs, could be one step on the very very long road to democratising healthcare – or at least part of it. In order to build their algorithm the team first made a database of nearly 130,000 skin disease images, and fed them into the algorithm as raw pixels with an associated disease label.

Bill Gates is the richest man in the world right now, with more than $85 billion to his name, and, according to one estimate, if he makes it to his mid-80's, he will likely be the world's first trillionaire . But self-made billionaire Mark Cuban predicts that the world's first trillionaires will actually be entrepreneurs working with artificial intelligence. "I am telling you, the world's first trillionaires are going to come from somebody who masters AI and all its derivatives and applies it in ways we never thought of," says the star investor of ABC's "Shark Tank," speaking to a packed house in Austin at the SXSW Conference and Festivals Sunday night. Ever faster computer processors and exponentially larger data sets are creating opportunity to apply artificial intelligence to new industries like insurance, says Cuban . We will "see more technological advances over the next ten years than we have over the last thirty. It's just going to blow everything away," says Cuban, who himself started out as the child of a blue-collar family from Pittsburgh.

As I noted in a February blog post, many enterprises today need solutions that couple high-performance computing with data analytics. This convergence of technologies is blurring the boundaries between HPC and big data, and clearing the way forward for the advent of high-performance data analytics (HPDA). In a parallel trend, enterprises increasingly need solutions that merge technologies for machine learning and deep learning -- a need I will explore more deeply in today's post. Machine learning was born from pattern recognition and the theory that computers can learn without being programmed to perform specific tasks. Researchers interested in artificial intelligence (AI) wanted to see if computers could learn from data and the process of iterative training on new data sets.

OK, lets make some money. Or at least get some perspective on how to seek opportunities in the fast growing machine-learning space. Lots of startups but where to invest? Well here is kind of a checklist of things to look at before you just throw some cash (or cryptocurrency) at a new startup your best friend discovered on the internet. "Machine learning is a trending topic today and for good reason. It has enormous potential to transform entire markets and industries. Successful machine-learning startups will be the ones targeting vertical applications with a clear need for the technology. The consumer packaged goods industry is a good example. Machine learning can more accurately predict inventory levels to better manage the supply chain, reduce inventory costs, minimize excess capacity requirements, and eliminate stockouts. According to an Accenture study, machine learning can lead to a 4.25x improvement in delivery times and a 2.6x improvement in supply chain efficiency. Significant manual intervention implies that there is a real opportunity to optimize with complex prediction algorithms. In the same supply chain example, today analysts estimate inventory needs based on some historical data but also a lot of intuition. By leveraging data like production times, sell through rates, and others, learning models could more accurately predict future needs. Startups need access to significant amounts of data to train machine learning models effectively. Companies that can either partner with large, established corporations to leverage their data to learn, or that create a product that entices users to input their own data, will win. Algorithms will continue to be open-sourced, which makes proprietary data mission-critical. Input and feedback to a system improves its accuracy and creates a moat. Therefore a product should incent humans to provide feedback on its predictions and recommendations. For instance, Facebook's photo-tagging algorithm learns from people who either accept or reject suggestions about who is in their photos…."

You're awake, you're sentient, you might even be upright. You're not comatose or dead, and it's reasonable to assume that if you were on some kind of powerful mind-altering drug then you wouldn't be reading this. The point is, you're here, and you're alive, so therefore you're conscious. OK then, since you're conscious and I'm conscious and everyone else is conscious, go ahead. Does it belong to the mind or the body, or does it exist outside both? Is consciousness part of our souls, or does it live in the things we create – our art, our music, our cities and wars? Could it be mechanical or electronic, and, if so, what makes it operate? Most pressingly of all, is it possible we have now made for ourselves a new kind of consciousness, one which exists independently? If so, then what the hell have we got ourselves into? The search for a definition of consciousness must lay claim to be the world's longest-running detective story. We've had our best minds on it ever since we developed brains big enough to ask questions and, still, we seem to be stumped. Plato and Aristotle couldn't fix it; Kant, Hume and Locke tried different angles; Schroedinger, Heisenberg and Einstein remained in awe before it. None of them came up with the final formula, the definitive, nailed-it for ever, silences-all-critics answer. Lately though, the hunt seems to have changed gear. Despite big differences about how best to conduct the search and where to look, several of the most persistent sleuths have found themselves disconcertingly close to agreement. No-one is yet at the stage when they are ready to call a press conference and announce to the world they have finally apprehended the suspect, but they have at least begun to converge on these two leads: the Omega Point and the Singularity. Pierre Teilhard de Chardin is an improbable prophet, partly because he's dead, and partly because he's still associated with a famous palaeontological fraud.

An autonomous Chevrolet Bolt is tested at General Motors' Technical Center in Warren, Michigan. When General Motors bought Cruise Automation for $581 million one year ago it was a head-turning deal that brought together a massive, traditional Midwestern carmaker with a tiny San Francisco startup. This quirky pairing may be working as Cruise CEO Kyle Vogt says automated GM vehicles controlled by Cruise technology ar going to be deployed "very quickly." Vogt, who began dabbling in self-driving car technology as a student at MIT in the early 2000s and has been called a "robot guru," has kept a low public profile the past few months amid nonstop announcements by others in the driverless car space. But in a recent conversation with Forbes he gave an update on Cruise's relationship with GM, progress on their automated vehicle program and the rush of competitive activity.

We now know that artificial intelligence (AI) can play a winning game of poker. But, alas, poker is a game of deceit, where one needs to remain calm and hold back any traces of emotions. In today's workplaces, it also helps to remain calm and hold back on emotions -- to an extent. But how many successful business leaders go through their days with constant poker faces? Alas, AI can't bring any of that good stuff to the table as of yet, and that is making it a hard sell.

Ask many interviewers about their least favorite part of the job and they'll almost always point to transcription. It can take hours to turn even a short chat into text, which is a serious pain for everyone from reporters to police interrogators. China tech giant Baidu may have a smarter approach: artificial intelligence. It just released a beta for SwiftScribe, a transcription app that uses a neural network to make sense of speech. The software not only promises relatively accurate speech-to-text processing thanks to training on "thousands of hours" of recordings, but learns from edits. It should account more for how people actually speak, saving you from making a load of edits.

A superhuman skill once the preserve of comic book heroes could soon become a reality. Scientists have used a combination of brain scanning and artificial intelligence to read the minds of'criminals' to determine whether they are guilty of knowingly committing a crime. This is the first time that neurobiological readings alone have been used to determine guilt, according to the study, and the findings could impact how we judge criminal responsibility in the future. A 2013 study found that researchers could predict how likely prisoners were to re-offend through brain scans. A team of neuroscientists at the Mind Research Network in Albuquerque studied a group of 96 male prisoners shortly before they were due to be released.

Dimensionality reduction is a critical component of any solution dealing with massive data collections. Being able to sift through a mountain of data efficiently in order to find the key descriptive, predictive and explanatory features of the collection is a fundamental required capability for coping with the Big Data avalanche. Identifying the most interesting dimensions of data is especially valuable when visualizing high-dimensional (high-variety) big data and when telling your data's story. There is a "good news, bad news" angle here. First, the bad news: the human capacity for visualizing multiple dimensions is very limited: 3 or 4 dimensions are manageable; 5 or 6 dimensions are possible; but more dimensions are difficult-to-impossible to assimilate.