Once the three-billion-letter-long human genome was sequenced, we rushed into a new'omics' era of biological research. Scientists are now racing to sequence the genomes (all the genes) or proteomes (all the proteins) of various organisms – and in the process are compiling massive amounts of data. For instance, a scientist can use'omics' tools such as DNA sequencing to tease out which human genes are affected in a viral flu infection. But because the human genome has at least 25,000 genes in total, the number of genes altered even under such a simple scenario could potentially be in the thousands. Although sequencing and identifying genes and proteins gives them a name and a place, it doesn't tell us what they do.

They continue to target computer networks and damage their infrastructure. Now, a combined team from MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL) and machine-learning startup PatternEx have developed a powerful artificial intelligence system called AI2 which works significantly better than any existing cyber-attack detection system. The system has been tested on 3.6 billion log lines or pieces of data that reveal major system activities triggered by millions of users over a period of three months. Researchers have found that new tool can detect cyber-attacks with 85% accuracy which is roughly three times better than the previous benchmark. Moreover, it reduces the number of'false positives' – an event wrongly identified as threat – by a factor of 5. Conventional security systems are either virtual machine-based or humanly operated but none of them has proven overwhelmingly successful at encountering cyber-attacks.

Apple's new robot, Liam, is designed to disassemble iPhones for recycling purposes. Meet Liam, an Apple robot designed to take apart 1.2 million iPhones a year. Mashable reporter Samantha Murphy Kelly got a first look at the robot at Apple's headquarters. It has 29 arms and it was an Apple secret for three years. "Liam is programmed to carefully disassemble the many pieces of returned iPhones, such as SIM card trays, screws, batteries and cameras, by removing components bit by bit so they'll all be easier to recycle. Traditional tech recycling methods involve a shredder with magnets that makes it hard to separate parts in a pure way (you'll often get scrap materials commingled with other pieces)."

If you are using data science for only one-time, ad-hoc analysis, then you are doing it wrong. There is no doubt that companies can benefit greatly from this type of one-time data science exercise and most start here. However, much more value is created when data science can be applied in real-time scenarios and in an ongoing manner. We can't just build a machine learning (ML) model and share the insights, we have to go to the next step and operationalize it, making it part of the fabric of our business processes and affecting outcomes in real-time. For example, what becomes possible when we can score human movement in real-time--like a system that can tell you that someone is currently running or moving at 30 MPH when they shouldn't be or just fell down on the floor.

Artificial Intelligence has the potential to revolutionize technology as we know it, although we're still a long ways away from Skynet (thank god). But with everyone from Google to Facebook to Elon Musk getting involved, how far away are we from that sci-fi future? We're getting closer, and whether that means robots eventually take over the world, or human and robot relations… your prediction is as good as mine. The potential for AI is practically limitless. What we are doing at Scope is trying to apply AI to revolutionize how we search for and share photos.

People learning new concepts can often generalize successfully from just a single example, yet machine learning algorithms typically require tens or hundreds of examples to perform with similar accuracy. People can also use learned concepts in richer ways than conventional algorithms--for action, imagination, and explanation. We present a computational model that captures these human learning abilities for a large class of simple visual concepts: handwritten characters from the world's alphabets. The model represents concepts as simple programs that best explain observed examples under a Bayesian criterion. On a challenging one-shot classification task, the model achieves human-level performance while outperforming recent deep learning approaches.

If you ask many people to name the technology categories that are creating sweeping change right now, cloud computing and Big Data analytics would probably be top of mind for a lot of them. However, there is an absolute renaissance goind on right now in the field of artifical intelligence and the closely related field of machine learning. Some of the biggest tech companies are helping to drive the trend, and Google added to the momentum on this front this week. Specifically, Sundar Pichai, Google's CEO, said on a conference call, "I do think in the long run we will evolve in computing from a mobile-first to an A.I.-first world." In this post, you'll find a collection of the most notable A.I. tools that have recently been open sourced.

Humans have a terrible track record of predicting financial crises in time to fend them off. Some computer scientists think that algorithms might help. Given the right information, some crises can be foreseen. In "The Big Short," Michael Lewis told the story of the scattered few who saw the imbalance growing in the mortgage market and profited as a result. Over decades, academic research has shown that many banking crises come with early warning signals, such as rapidly increasing debt and leverage.

Leading investment crowdfunding platform OurCrowd has registered its 6th successful exit as portfolio company Crosswise has been purchased by Oracle. The transaction was revealed in Israeli media on April 15th revealing that Oracle paid 50 million for the "machine learning based cross-device data" company. The transaction closed on April 14th. OurCrowd, and its registered investors, participated in a Series A funding round that provided 670,392 to the young company. Co-investors on the funding round included Giza Venture Capital, Horizons Ventures and a "high profile angel group."

Perhaps the most important news of our day is that datasets--not algorithms--might be the key limiting factor to development of human-level artificial intelligence. At the dawn of the field of artificial intelligence, in 1967, two of its founders famously anticipated that solving the problem of computer vision would take only a summer. Now, almost a half century later, machine learning software finally appears poised to achieve human-level performance on vision tasks and a variety of other grand challenges. What took the AI revolution so long? A review of the timing of the most publicized AI advances over the past thirty years suggests a provocative explanation: perhaps many major AI breakthroughs have actually been constrained by the availability of high-quality training datasets, and not by algorithmic advances.