This blog follows as a full retraction from the earlier blog, "Chatbots will be smart but artificial intelligence doesn't exist yet," which claimed AI is not real or useful. It is both of those things and neither of them. Full retraction stated and received! "Chatbots will be smart, well-informed even but not artificially intelligent. Computers cannot think by themselves. They don't learn on their own. No!" That was the opening statement of the previous blog.

There are a lot of buzzwords thrown around when it comes to artificial intelligence, but not a lot of knowledge about what they are. One way of thinking about artificial intelligence is to define it simply as algorithms we don't quite understand yet. For example, 20 years ago finding point-to-point directions in something such as Google Maps would have been considered artificial intelligence. These days, pathfinding algorithms are a part of core curriculum for most computer science undergraduates. As algorithms advance, it's no longer seen that way.

This robot at the Institute for Artificial Intelligence can make popcorn. But is AI a topic that CEOs need to worry about today? If you're a CEO and sometimes think your technical team speaks a different language, you're not alone. My world is saturated with tech jargon because I am a venture capitalist who reviews and meets with hundreds of tech startups every month. But which buzzwords are the most important for a CEO to understand?

With growing criticism over misinformation in search results, Google is taking a harder look at potentially "upsetting" or "offensive" content. SAN FRANCISCO -- With growing criticism over misinformation in search results, Google is taking a harder look at potentially "upsetting" or "offensive" content, tapping humans to aid its computer algorithms to deliver more factually accurate and less inflammatory results. The humans are Google's 10,000 independent contractors who work as what Google calls quality raters. They are given searches based on real queries to score the results, and they operate based on guidelines provided by Google. On Tuesday they were handed a new one: to hunt for "Upsetting-Offensive" content such as hate or violence against a group of people, racial slurs or offensive terminology, graphic violence including animal cruelty or child abuse or explicit information about harmful activities such as human trafficking, according to guidelines posted by Google. The goal: to steer people with queries such as "did the Holocaust happen" to trustworthy websites and not to websites that engage in falsehoods or hate speech.

As we discussed in a previous article, "Analytics in The Stock Market: Made for Each Other," the financial services industry would seem to be a particularly good fit for current advances in artificial intelligence (AI) and machine learning. After all, financial services are based on understanding risk and balancing a wide range of numeric factors as well as predicting trends. However, uptake of machine learning and AI has been relatively slow in this industry. The chief reason is the need to maintain a conservative outlook and to accommodate a wide range of processes and systems based around legacy information technology. Despite this, we will likely see some change soon.

As organizations look to become more agile in the age of digital business, one of the first issues they routinely encounter is how rigid their supply chains are. Most supply chains are designed to optimize the delivery of a handful of variations of a product at a time when customers are demanding more customization than ever. To help organizations inject some flexibility into those supply chains, SAP has announced it has infused its SAP Business Planning Suite of software with a suite of machine learning algorithms it has packaged together under a SAP Clea brand name. Martin Barkman, vice president and global head of digital business planning for SAP, says SAP Clea takes supply chains past predictive analytics to give organizations access to software that over time learns how to optimize the supply chain. Armed with that capability, Barkman says, organizations can then segment supply chains to drive additional business opportunities in the most efficient way possible.

But I'm not going to talk about how they work on an implementation, nuts and bolts level. Instead I'm going to provide a high-level framework for conceptualising all of these operations, which will hopefully provide some insight with which you'll be able to visualise these otherwise complicated, mathematical and rather opaque procedures in a more intuitive way. The core of this approach is to think of all of these operations as journeys through multiple dimensions and transformations in space. To actually implement these algorithms, it is of course essential to understand the lower level nuts and bolts, the maths, the architectures, training algorithms, hyper-parameters etc. But given a decent implementation and API, I don't think it is necessary to understand the lower level nuts and bolts just to use the algorithms.

The two words that can send goosebumps to every college basketball fan in the country. It's the month where every fan will fill out a bracket, each thinking that they picked the right 12 over 5 seed upset or that they were the only one to pick the Cinderella team that makes it to the Elite 8. The month where people will spend hours watching regular season games, pouring over stats and expert analysis, trying to carefully predict the most likely winner, only to find their pick lose in the first round (Thanks Michigan State). The month where your younger sister ends up having a better bracket than you because she picked the teams with the "cooler" mascots (Sad, but true story). March Madness is the sports phenomenon that incites anxiety, regret, elation, and every other possible emotion in the spectrum. And it's about to start in 4 days.

Machine learning, task automation and robotics are already widely used in business. These and other AI technologies are about to multiply, and we look at how organizations can best take advantage of them. Businesses are scrambling to use artificial intelligence and trying to work out how best to put digital assistants to work. Consumers are wondering what impact these new technologies will have on their everyday lives. From business to leisure, we interact with AI when we use an app to turn on a light bulb, talk to Google Home or Amazon Alexa, or write better emails.

We've seen many buzzwordy innovations in technology over the last decade, from cloud computing to big data to microservices and beyond - but artificial intelligence (AI) by far has the most buzzword baggage. On the one hand, AI is perhaps the most revolutionary set of innovations since the transistor. But on the other, the bad press surrounding it continues to mount, perhaps even faster than the innovations themselves. We didn't suffer this kind of PR nightmare with the cloud, or the web, or even client/server. In fact, AI has an unprecedented set of PR challenges that threaten to sink the entire movement.