Pepper, the humanoid robot, is coming up with all sorts of ways to serve its human overlords, including in eldercare. The little rolling robot took to the stage at the Disrupt SF Hackathon today to show how it could help our aging parents, should they fall and not be able to get up. According to PepperHealth creators Ethan Fan and Angelo Hizon – both Battlefield NY 2015 veterans for fitness app VimoFit – PepperHealth will also detect a rapidly rising heart rate, check in on how you're feeling, follow up on your sleeping habits and remind you to take your meds -- the robot will even call up an Uber for those unable to use a smartphone. It works by detecting abnormal vitals through the Watch such as a rapid heart rate or a fall and then Pepper "wakes up" and comes over to check out what's going on. Should something serious happen, Pepper will alert a built-in emergency system and give medics the elder's location to get them immediate assistance.

During the christmas vacation of 2015, I got a motivational boost again and decided try out Kaggle. So I spent quite some time experimenting with various algorithms for their Homesite Quote Conversion, Otto Group Product Classification and Bike Sharing Demand contests. The main takeaway from this was the experience of iteratively improving the results by experimenting with the algorithms and the data. I learned to trust my logic when doing machine learning. If tweaking a parameter or engineering a new feature seems like a good idea logically, it's quite likely that it actually will help.

Thus far, the dangers associated with self-driving cars have been largely relegated to their ability to safely navigate our roads and highways. But now the U.S. Department of Justice is interested in taking a closer look at a different kind of danger altogether -- and this has nothing to do with how well an autonomous vehicle can brake or steer. Rather, a new threat analysis team has been tasked with determining the potential security issues associated with not only self-driving cars, but other Internet of Things and connected devices as well. After all, if you can control everything from the palm of your hand, who's to say someone else can't do the same thing? Under Assistant Attorney General John Carlin, who heads the Justice Department's national security team, the new team seeks to secure the internet of things and protect it from potential terrorist threats.

The 1980s at the MIT Computer Science and Artificial Intelligence Laboratory seemed to outsiders like a golden age, but inside, David Chapman could already see that winter was coming. As a member of the lab, Chapman was the first researcher to apply the mathematics of computational complexity theory to robot planning and to show mathematically that there could be no feasible, general method of enabling AIs to plan for all contingencies. He concluded that while human-level AI might be possible in principle, none of the available approaches had much hope of achieving it. In 1990, Chapman wrote a widely circulated research proposal suggesting that researchers take a fresh approach and attempt a different kind of challenge: teaching a robot how to dance. Dancing, wrote Chapman, was an important model because "there's no goal to be achieved. You can't win or lose. It's not a problem to be solved…. Dancing is paradigmatically a process of interaction."

If you've been following the Drift blog, you've probably noticed that we've been writing quite a bit about artificial intelligence and chatbots in recent weeks (like this post about how artificial intelligence could change marketing forever, and this one that describes the different types of chatbots ... oh, yeah, and this one where I defined machine learning in terms non-technical people can understand). Anyway, after doing a bunch of research around these topics, and uncovering some pretty interesting statistics along the way, it dawned on us: Why not take everything we've learned so far and put it altogether in one big infographic? Check out our "Rise of the Chatbots" infographic below and let us know what you think. Feel free to use the embed code at the bottom for adding the infographic to your own blog or website, too. Once a week or so we send an email to 8,000 people from companies like HubSpot, Twitter, Pinterest, and more with our best content.

Siri was first introduced in 2011 on the iPhone 4S. When it was first introduced it was fun to play with and receive cute responses from, but it was not too useful. Since then, Siri and its competitors have become increasingly more useful. These phone voice assistants are just a small glimpse of what artificial intelligence could look like in the future. The singularity is a theory of a time in the future when humans create a true artificial super-intelligence.

How is the Internet of Things, or IoT, changing trends in cognitive business? The impact is evident with the alliance between computers and humans. Computers still quickly assimilate and spew out data based on what is seen, heard and read. The difference is that they are now reasoning, understanding, and learning from those processes, whether using older, stored information or real-time data. Swift, accurate decisions are based on facts rather than emotions or circumstances.

According to Musk, only one company scares him (he would not mention names, but Google is in the "pipe"). The danger is the following: an organization (company, group of individuals, government, etc ..) manages to create an AI with great intelligence and uses it for its own purposes. According to the intentions of the owner, this monopoly could lead to very negative consequences for our society.

A few years ago, Michael Osborne and a colleague at Oxford University caused a stir when they published research suggesting 47 percent of jobs in the U.S. are at risk of being replaced with robot labor. Subsequent studies suggest closer to 10 percent of jobs in developed countries could be automated, which is only marginally less worrying for workers. What isn't in doubt is that advances in algorithms and robotics will transform the workplace, with both rote manual labor and higher-level cognitive tasks soon to be performed by machines. Robotics companies, keen to avoid the insinuation their products take jobs from humans, talk a lot these days about "co-bots" (collaborative robots). Humans and robots will increasingly collaborate, they say, with humans freed to do more productive, fulfilling tasks thanks to machines taking on the grunt work.

Throughout all the uncertain economic news of the past few years, one trend has stayed constant: it's increasingly hard to find and attract good candidates for many of the roles that global companies require. This competition for the right people has pushed HR and business-line leaders alike to be more discerning in their candidate selection, and to find new approaches to assessing an applicant's likelihood of success. And the recent use of algorithmic assessments and machine learning has helped recruiters screen large volumes of applicants. But, as with any technology, it still requires human oversight. Some business commentators have also questioned how useful this approach to recruiting is.