I hope you are having a fabulous February so far. Depending on your industry, you may already be deeply invested in exploring AI, machine learning and robotics. Or you might suspect you should be (headlines like "Robots Will Steal Your Job" are not uncommon), whereas the truth is, you're still trying to get the privacy settings on your phone right! Either/Or, or somewhere in the middle, I thought I would share a great source of information and an enlightening overview with you. And explore what this might mean for your career and your business?

A machine learning system is particularly advantageous in a field like this, as you don't have to outline exactly what you're looking for. You can identify the exact conditions that prompt a transition without knowing what they are, and theoretically spot previously undiscovered transitions. There's a lot of work to be done. It's easy to detect known transitions in a lab, where you can limit the number of particles, but it's much harder when you're looking at the overwhelming volume of particles in real life. If researchers can achieve that feat, though, they could discover reproducible behavior that might be useful in products, such as superconductors with more forgiving properties.

Theoretical physicists from ETH Zurich deliberately misled intelligent machines, and thus refined the process of machine learning. They created a new method that allows computers to categorize data--even when humans have no idea what this categorization might look like. When computers independently identify bodies of water and their outlines in satellite images, or beat the world's best professional players at the board game Go, then adaptive algorithms are working in the background. Programmers supply these algorithms with known examples in a training phase: images of bodies of water and land, or sequences of Go moves that have led to success or failure in tournaments. Similarly to how our brain nerve cells produce new networks during learning processes, the special algorithms adapt in the learning phase based on the examples presented to them.

Hewlett Packard Enterprise (HPE) has been very busy since the separation of HP Inc. and HPE, doing spin-mergers, spin-outs and resetting for a much leaner and faster future. This never meant they wouldn't acquire companies; in fact, they have indicated a few areas where this made sense, one of them being edge security. Two weeks ago, HPE announced their acquisition of Niara, who many consider an up-and-comer, focusing on User and Entity Behavior Analytics (UEBA). Integrating with HPE's existing Aruba ClearPass portfolio, Niara's solutions leverage big data analytics and machine learning to help businesses better protect their enterprises against next-gen cyberattacks. In the increasingly interconnected, IoT-driven world, the need for advanced, effective security is rapidly growing.

A nonprofit group is testing this drone to see how fast it could get medications from a town to a remote village in Peru that's six hours away by boat. A nonprofit group is testing this drone to see how fast it could get medications from a town to a remote village in Peru that's six hours away by boat. If a snake bites you in a remote Amazonian village like Pampa Hermosa, Peru, and the local doctor is out of the right anti-venom, it might be wise to prepare some goodbyes. The nearest resupply, in a town called Contamana, is up to six hours away by riverboat, and you might not last that long. But you might last 35 minutes, the travel time between Pampa Hermosa and Contamana as the drone flies. A single unmanned aerial vehicle or UAV could dart over the lush canopy with a vial of lifesaving anti-venom, and a nonprofit called WeRobotics is trying to make that a reality.

Stacking (also called meta ensembling) is a model ensembling technique used to combine information from multiple predictive models to generate a new model. Often times the stacked model (also called 2nd-level model) will outperform each of the individual models due its smoothing nature and ability to highlight each base model where it performs best and discredit each base model where it performs poorly. For this reason, stacking is most effective when the base models are significantly different. Here I provide a simple example and guide on how stacking is most often implemented in practice. Feel free to follow this article using the related code and datasets here in the Machine Learning Problem Bible.

MIT announced today that it's developed a speech recognition chip capable of real world power savings of between 90 and 99 percent over existing technologies. Voice technology has, of course, become nearly ubiquitous in mobile devices, thanks to the exponential growth of smart assistants like Siri, Alexa and Google Home – but the new chip could help branch out in much simpler electronics. The team gives IoT devices a potential use case – devices designed to go months on end without charging or changing batteries. Speech input will become a natural interface for many wearable applications and intelligent devices. The miniaturization of these devices will require a different interface than touch or keyboard.

Artificial intelligence has been a dream in technology ever since Alan Turing first wrote his seminal paper, Computing Machinery and Intelligence, Now, thanks to advances in hardware power and algorithm design, AI is a growth industry – and it has no shortage of vocal advocates. These are some of the most vocal and influential leaders working on artificial intelligence, robotics, chat bots, virtual reality, the ethics of autonomous software and vehicles and more. Organizes the London.AI meet up and the annual Playfair AI Summit. Thanks to all who kicked off discussion on the back of my piece on "6 areas of #AI/ML to watch closely" Keep going! Designs intelligent systems into working AI systems to help understand natural intelligence.

Acast, a technology platform for on-demand audio and podcasting, has launched Recommendations: a function that utilises a machine learning algorithm to surface new, tailored content for its users, similar to Spotify's Discover. During the feature's beta phase, Acast extracted data over a period of two weeks which showed that users are 52% more likely to follow a show if it is recommended to them by the algorithm, and 49% more likely to listen to multiple episodes of a show recommended by Acast. Johan Billgren, Acast CTO, comments: "Searching for new podcasts is hard and often time-consuming. We want our users to spend their time listening to great podcasts, rather than looking for them, and that is why we are launching Recommendations. Our machine learning algorithm, which we began testing in October last year, gets to know preferences over time by learning from a user's choices and then recommending new shows that they will like."

It's looking increasingly likely that artificial intelligence (AI) will be the harbinger of the next technological revolution. When it develops to the point wherein it is able to learn, think, and even "feel" without the input of a human – a truly "smart" AI – then everything we know will change, almost overnight. That's why it's so interesting to keep track of major milestones in the development of AIs that exist today, including that of Google's DeepMind neural network. It's already besting humanity in the gaming world, and a new in-house study reveals that Google is decidedly unsure whether or not the AI tends to prefer cooperative behaviors over aggressive, competitive ones. A team of Google acolytes set up two relatively simple scenarios in which to test whether neural networks are more likely to work together or destroy each other when faced with a resource problem.