Late last month it was revealed that Apple is quietly working on the iPhone 8 in its Herzliya, Israel offices. Today, it's been revealed that the Cupertino giant is working on its car operating software in its Kanata, Canada facility. The location is somehow strategic, since the tech company hired ex-employees of its former rival, BlackBerry, to work on this project. According to MacRumors, Apple's R&D facility in Canada is focused on the "software core" of its upcoming self-driving car program that is currently being developed by a separate Project Titan team. The car operating system is said to come with many features, such as a heads-up display that would be very useful to drivers who want to access Maps via Apple's digital assistant, Siri.

Slack is going to tap into IBM pet Watson and its cognitive computing skills, covering both bots and other conversation inferences. Slack's own Slackbot will be the first to get the intelligence makeover, with IBM and Slack looking to share what they learn from the experience with other developers. The companies believe integrating Watson will improve accuracy and efficiency of troubleshooting with the bot. IBM is also working on a Watson-powered Slack chatbot specifically for IT and network issues. IBM's Watson is really sharing its wisdom around: it's just joined the Weather Channel's bot on Facebook Messenger, where it will learn your preferences and offer up personalized forecasts and even news for to US-based bot chatters.

The company's scientists think its new AI-based factotum will be the biggest thing since search. It is the day after Google's big hardware event in San Francisco, when the company formally unveiled a new phone (a jab to the iPhone) and a voice-activated speaker (a gut punch to Amazon's Echo). Word of mouth is already tracking positive; a countdown to ecstasy, in the form of upcoming rhapsodic reviews of the Pixel phone, has already begin. But in a conference room on the company's sprawling Mountain View campus, Fernando Pereira, who leads Google's projects in natural language understanding, is less excited about his company's shiny new devices than he is about what will happen when people use them. "Let me tell you a little bit about The Transition," he says.

Intelligent machines powered by AI computers that can learn, reason and interact with people are no longer science fiction. Today, a self-driving car powered by AI can meander through a country road at night and find its way. An AI-powered robot can learn motor skills through trial and error. This is truly an extraordinary time. In my three decades in the computer industry, none has held more potential, or been more fun. The era of AI has begun.

Databricks Inc., the primary commercial steward behind the popular open source Apache Spark data processing framework for Big Data analytics, published a new report indicating the technology is still red-hot, driven by more use of SQL, streaming analytics and machine learning. The company this summer polled more than 900 organizations and solicited data from 1,615 respondents -- mostly Spark users -- coming from the ranks of data scientists, data engineers, architects and others, and last week published the results in the Apache Spark Survey 2016 Report (free download upon providing registration info). The report follows up on a similar survey last year, confirming the technology's widespread popularity as the most active open source project in the Big Data space. "As in 2015, which was a tremendous year in growth for Apache Spark, this year, too, its growth remains unabated -- not only in areas like the public cloud, but also with the increased use of Spark Streaming and the use of machine learning," the report states. "2016 also shows Spark's robust adoption across a variety of organizations and users from many functional roles to build complex solutions, using multiple Spark components."

Apple revealed its fourth quarter financial results during a conference call with investors on Tuesday, but perhaps the most interesting narrative -- aside from the company's first annual revenue decline since 2001 -- was the tech giants avowed focus on machine learning. "Today, machine learning drives improvement in countless features across our products," Apple CEO Tim Cook boasted during his initial remarks. Cook then offered a state of machine learning at Apple, by running down areas where machine learning is already helping to improve the iPhone user experience, noting that machine learning "enables the proactive features in iOS 10, and that cameras employ it in face recognizing software. Machine learning is also a key aspect of Apple's fitness offerings. "Machine learning continually helps Siri get smarter in areas including understanding natural language," Cook added.

Which of the following are substantially the same things? For as precise a profession as we data scientists purport to be we are sometimes way too casual with our language. Read several articles about AI, Deep Learning, and Machine learning and you will come away confused whether these are all the same or all different. Imagine how confused non-data scientists must be. The truth is that each of these terms has some overlap in a Venn diagram but none of these is a perfect subset of the other and none completely explains the others.

I hope you enjoy them, and it would be great if you would leave brief reviews of these books in the comments below – I'm sure all the authors would appreciate your comments and shares. About the Author Lee Baker is an award-winning software creator with a passion for turning data into a story. A proud Yorkshireman, he now lives by the sparkling shores of the East Coast of Scotland. Physicist, statistician and programmer, child of the flower-power psychedelic '60s, it's amazing he turned out so normal! Turning his back on a promising academic career to do something more satisfying, as the CEO and co-founder of Chi-Squared Innovations he now works double the hours for half the pay and 10 times the stress - but 100 times the fun! He also wanted to be rich, famous and good looking.

We are building an AI assistant that helps you stay on top of all your conversations and reconnect with the right people at the right time. As you interact across different communication channels (Facebook, Slack, Texting, Email), Fireflies.ai My experiences at Penn created a unique opportunity for me to pursue this idea, and the lessons I learned there are imbued into the culture of our startup and our core product. During my time here, I was fortunate enough to meet very talented and hardworking peers from diverse backgrounds. Learning from others and sharing unique experiences is what makes Penn special.

While the number of headlines about machine learning might lead one to think that we just discovered something profoundly new, the reality is that the technology is nearly as old as computing. It's no coincidence that Alan Turing, one of the most influential computer scientists of all time, started his 1950 treatise on computing with the question "Can machines think?" From our science fiction to our research labs, we have long questioned whether the creation of artificial versions of ourselves will somehow help us uncover the origin of our own consciousness, and more broadly, our role on earth. Unfortunately, the learning curve on AI is really damn steep. By tracing a bit of history, we should hopefully be able to get to the bottom of wtf machine learning really is.