It's hard to believe that Black Friday and the 2016 holiday shopping season are almost here. The good news for those of us shopping for personal consumer electronics and tech gadgets for friends and family this holiday season is that there are some real bargains. Tech innovation has improved dramatically over the last year and because of competition the pricing has come down significantly. This year, I've structured the holiday tech gift guide a little differently. I had the opportunity to review many products this year, but I've narrowed the guide down to my top 30 featured products -- from tablets and phones to cameras and drones. I'm also putting together a future gift guide of less costly tech items in categories like kids, health/fitness, home/security and audio so be on the lookout for that. Always remember to check online for the lowest current prices on these items as most will be discounted as we get closer to the holidays. Remember, Hanukkah begins on the evening of December 24 this year. The Surface Pro 4 is the perfect holiday gift for a loved one looking for a device for work or play -- especially for the student who doesn't want to schlep around a heavy computer laptop. Powered by an Intel Core processor, it offers up to nine hours of battery life. Paired with the $60 Surface Pen you can use it for detailed sketches and handwritten notes.

Samsung Electronics wants to buy its way into the connected car market, with a plan to acquire Harman for US$8 billion. It's the latest in a line of acquisitions by Samsung, as it seeks to diversify its business beyond the slowing smartphone market. Other recently announced deals include last month's buy of artificial intelligence startup Viv Labs, which has developed a virtual personal assistant Samsung hopes to put in its consumer electronics products, and the June purchase of Joyent, a supplier of cloud services for the internet of things. Samsung only set up its automotive electronics team last December, with the goal of identifying business opportunities in the sector, and where previous acquisitions have been unabashedly about enhancing or adding capabilities to existing Samsung products, the strategy with Harman seems to be more about buying a position in a new market. Harman's best-known products are probably the headphones, Bluetooth speakers and home hi-fi systems it sells under brands such as JBL, AKG and Harman Kardon, but it makes around two-thirds of its revenue from audio electronics.

Facebook's deep-learning artificial intelligence systems have learned to recognize your friends in your photos, and Google's AI has learned to anticipate what you'll be searching for. But there's no need to feel left out, even if your company's computers haven't learned much lately. A growing number of tech giants and startups have begun offering machine learning as a cloud service. That means other companies and startups do not need to develop their own specialized hardware or software to apply deep learning--the high-powered version du jour of machine learning--to their specific business needs. "Deep-learning algorithms dominate other machine-learning methods when data sets are large," says Zachary Chase Lipton, a deep-learning researcher in the Artificial Intelligence Group at the University of California, San Diego, who has examined cloud AI services from companies such as Amazon and IBM.

It is no doubt that the sub-field of machine learning / artificial intelligence has increasingly gained more popularity in the past couple of years. As Big Data is the hottest trend in the tech industry at the moment, machine learning is incredibly powerful to make predictions or calculated suggestions based on large amounts of data. Some of the most common examples of machine learning are Netflix's algorithms to make movie suggestions based on movies you have watched in the past or Amazon's algorithms that recommend books based on books you have bought before. So if you want to learn more about machine learning, how do you start? For me, my first introduction is when I took an Artificial Intelligence class when I was studying abroad in Copenhagen.

Data is everywhere – masses of it. And it's helping businesses to make better decisions across departments. Marketing can utilise data to discover the effectiveness of email campaigns, finance can analyse past trends to make predictions and projections for the future, and sales can target their follow-up with detailed information on prospective customers. But data is only useful when business tools transform it into valuable information. Data intelligence through algorithms and analytics make business data relatable. The most advanced solutions require enormous amounts of data to be able to offer accurate insight to users.

Ben Dickson is a software engineer and the founder of TechTalks. More posts by this contributor: Why it's so hard to create unbiased artificial intelligence How to facilitate the path to brownfield IoT development Why it's so hard to create unbiased artificial intelligence How to facilitate the path to brownfield IoT development Why it's so hard to create unbiased artificial intelligence Computers and mobile devices running rich operating systems have a plethora of security solutions and encryption protocols that can protect them against the multitude of threats they face as soon as they become connected to the Internet. Such is not the case with IoT. Of the billions of IoT devices presently in use, a considerable percentage are sporting low-end processing power and storage capacity and don't have the capability to become extended with security solutions. Yet they are connected to the Internet, nonetheless, which is an extremely hostile environment.

Last week, over 50,000 people from 166 countries made their way to Web Summit in Lisbon, Portugal. With 21 stages dedicated to different topics and sectors, there was never a lack of choice; however, despite the huge variety of talks, several themes repeatedly emerged. At Web Summit, the narrative was less around what is currently possible and more around managing our expectations for the near future. Yes, artificial intelligence has huge possibilities, but we are still decades from having an emotional AI, said Gary Marcus, CEO at Geometric Intelligence, during the Future of the Worker panel. Rana el Kallouby, CEO of Affectiva, echoed Marcus' sentiment and reminded us to think of the inputs as we build AI solutions.

Doing machine learning on distributed data sets is methodologically similar to working with data that fits in-memory--train your algorithm on a subset of the data, validate on another subset, and finally test with a different subset. In this post, we'll discuss how to pull data from BigQuery (the no-ops data warehouse that is part of Google Cloud Platform) into machine-learning-ready data sets. We'll use Airline Ontime Performance data, a 70 million row data set from the U.S. Bureau of Transportation statistics, that is available to all users in BigQuery as the airline_ontime_data.flights data set. The RAND() function returns a value between 0–1, so approximately 80% of the rows in the data set will be selected by this query. You want to create three data sets: training, validation, and testing, and while you got 80% of the data above, it is not nearly as easy to get the 20% that were not selected, let alone split that data into two parts. The RAND() function returns different things each time it is run, so if you run the query again, you will get a different 80% of rows.

Recently, I needed to book a lunch meeting. To help coordinate, I asked Amy to assist and cc'd her on the email. "Amy," I wrote, "please help us find a time to meet. Let's plan for sushi at Tokyo Express on Spear Street." Amy looked at my calendar, found an open time suitable for everyone invited, and booked the meeting.

With the company valued at over $62 billion and running in over 523 cities around the world with plans to launch a fleet of autonomous cars, Uber is constantly looking to disrupt and innovate. And they are leading the pack when it comes to the use of machine learning in their strategy. At the Kaizen Data Conference hosted at Galvanizes' San Francisco campus, Uber's Head of Machine Learning, Danny Lange, discussed past, present, and future advancements in his area of expertise. This compelling talk covers artificial intelligence's central role in business disruption and innovation, from Uber's self-driving cars to emerging new technologies.