Tom Frangione is the chief operating officer of Greylock Partners. Today more than ever, technology is transforming how enterprises do business. The IT profession has evolved from being reactionary to proactive, and CIOs now have a seat at the table to drive company strategy. Greylock COO Tom Frangione catches up with Kim Stevenson, Intel COO for the Client and Internet of Things Businesses and Systems Architecture Group, to discuss how startups can sell to larger enterprises, the importance of IT innovation and what the future of machine learning means for enterprise companies. CIOs are becoming the expert source on enterprise trends and new innovative solutions to help companies gain and maintain a competitive advantage.

What are the advantages of different classification algorithms? For instance, if we have large training data set with approx more than 10,000 instances and more than 100,000 features, then which classifier will be best to choose for classification? This list may seem a bit daunting because there are many issues that are not straightforward to answer. The good news though is, that as many problems in life, you can address this question by following the Occam's Razor principle: use the least complicated algorithm that can address your needs and only go for something more complicated if strictly necessary. To read the full article (posted as a Quora question, including 22 answers), click here.

A few years ago, a young man named Kyle Behm took a leave from his studies at Vanderbilt University in Nashville, Tennessee. He was suffering from bipolar disorder and needed time to get treatment. A year and a half later, Kyle was healthy enough to return to his studies at a different university. Around that time, he learned from a friend about a part-time job. It was just a minimum-wage job at a Kroger supermarket, but it seemed like a sure thing. His friend, who was leaving the job, could vouch for him. For a high-achieving student like Kyle, the application looked like a formality. But Kyle didn't get called in for an interview. When he inquired, his friend explained to him that he had been "red-lighted" by the personality test he'd taken when he applied for the job. The test was part of an employee selection program developed by Kronos, a workforce management company based outside Boston.

In less than a month since launching their powerful new website (http://www.2016tweets.live) In addition to this epochal milestone, the company recently began tracking and reporting on the number of bots that follow, like, and retweet the two major accounts. As bots can confuse and often mislead voters into thinking one party has more support than the other, this analysis allows the public to see the competing candidates on a more even playing field. So far Aicial has analyzed over 109,000 unique Twitter accounts that are engaging with Clinton and Trump. Aicial Co-Founder and CEO, Troy Kelly said the company is committed to further development of the social engine and is quite interested in discussing potential partnerships with news and media outlets.

Retailers are looking towards artificial intelligence (AI) such as image recognition technologies to help enhance sales staff productivity as well as make them be more competitive against their peers. Alexander Laugomer, project manager digital merchandising at consumer and industrial goods firm Henkel, says: "The major benefit of the AI technology is that it not only provides us with information on our own and our competitors' products, but it also gives us an actionable report straight to our mobile devices. "This allows our sales reps to improve our brands' situation in-store there and then, without the time-consuming task of manually compiling data." Henkel outsources this function to retail image-recognition firm Trax. The technology works by being able to recognise more than eight million images on a shelf.

Scientists at the Stanford University have released the first report of their One Hundred Year Study on Artificial Intelligence (AI100), in which they try to predict the potential impacts of artificial intelligence on human life in the long run. Known as "Artificial Intelligence and Life in 2030," the report comes just two years since the researchers began their work. It focuses on the history of AI technology and how it is being used in various fields today, such as in the development of robots for medical purposes and self-driving vehicles for transportation. Experts believe the Stanford study is important not only for researchers but for policymakers as well who may need to create new laws that would better cater to such technological advancements. AI100 is the brainchild of Eric Horvitz, managing director of Microsoft Research's Redmond laboratory.

South Korean Conglomerate LG Electronics announced that it will heavily invest in robot technology and artificial technology according to a report by Reuters Sunday. LG, Samsung's rival, is seeking to take advantage of recent advances in artificial intelligence which have the potential of creating sophisticated machines that can perform mundane everyday human tasks. LG, in a statement, said that its appliances division is preparing to enter the robotics industry, like Apple, Google, and Samsung, to create products which can integrate with their appliances products such as refrigerators, washers and air conditioning units. "We will prepare for the future by aggressively investing in smart home, robots and key components and strengthen the home appliances business's capabilities," said Jo Seung-jin, head of LG's appliances business, in the statement. Advances in artificial intelligence and wireless communications are making it possible for machines that are wifi-enabled to exchange information with each other and perform independent tasks.

Toshiba Corp. and Canon Inc. are starting separate drone-based services for corporate clients. Toshiba said Monday it will partner with auto parts manufacturer Alpine Electronics Inc. to commercialize a system by March 2018 to inspect remote infrastructure such as power cables and steel towers. Their service will combine Alpine Electronics' satellite navigation knowledge and Toshiba's digital image processing and artificial intelligence technologies. Camera and printer maker Canon Inc. meanwhile announced on Monday an investment in Prodrone Co., an Aichi Prefecture-based firm that manufactures drones for industrial use. Canon and Prodrone plan to sell drones loaded with Canon's cameras for farming, infrastructure inspection and surveillance purposes.

Today, machine learning underlies a range of applications we use every day, from product recommendations to voice recognition--as well as some we don't yet use everyday, including driverless cars. It is the basis of the new approach in computing where we do not write programs but collect data; the idea is to learn the algorithms for the tasks automatically from data. As computing devices grow more ubiquitous, a larger part of our lives and work is recorded digitally, and as "Big Data" has gotten bigger, the theory of machine learning--the foundation of efforts to process that data into knowledge--has also advanced. In this book, machine learning expert Ethem Alpaydin offers a concise overview of the subject for the general reader, describing its evolution, explaining important learning algorithms, and presenting example applications. Alpaydin offers an account of how digital technology advanced from number-crunching mainframes to mobile devices, putting today's machine learning boom in context.

There are few subjects in computing as fascinating, or intimidating, as machine learning. Let's face it -- mastering machine learning isn't something you can do in a weekend, and at the very least it requires a good grasp of the underlying mathematical principles. That said, if you've got the math chops, you'll want to augment your use of machine-learning frameworks (of which there are plenty to pick from now) with a good understanding of the theory behind them. Here are five high-quality, free-to-read texts that provide introductions to and explanations of machine learning's ins and outs. Some have code examples, but most focus on formulas and theory, meaning they can in principle be applied to any number of languages, frameworks, or problems.