If you are looking for an answer to the question What is Artificial Intelligence? and you only have a minute, then here's the definition the Association for the Advancement of Artificial Intelligence offers on its home page: "the scientific understanding of the mechanisms underlying thought and intelligent behavior and their embodiment in machines."
However, if you are fortunate enough to have more than a minute, then please get ready to embark upon an exciting journey exploring AI (but beware, it could last a lifetime) …
The American criminal justice system couldn't get much less fair. Across the country, some 1.5 million people are locked up in state and federal prisons. More than 600,000 people, the vast majority of whom have yet to be convicted of a crime, sit behind bars in local jails. Black people make up 40 percent of those incarcerated, despite accounting for just 13 percent of the US population. With the size and cost of jails and prisons rising--not to mention the inherent injustice of the system--cities and states across the country have been lured by tech tools that promise to predict whether someone might commit a crime.
Computers are built to process data, but there's a particular form of information so rich and dense in meaning that it's beyond the full comprehension of even the most advanced AI. It's also one that you and I process intuitively and deal in every day: language. Understanding the written and spoken word is a big an important challenge for computer scientists. This month, a small milestone was passed when a pair of teams from Microsoft and Alibaba independently created AI programs that can outperform humans in a reading comprehension test. As you might expect, this news resulted in a flurry of coverage.
Don't be a data scientist whose models fail to get deployed! An epic example of model deployment failure is from Netflix Prize Competition. In a short story, it was an open competition. Participants had to build a collaborative filtering algorithm to predict user rating for films. The winners received grand prize of US$1,000,000.
Today I got an email with a question I've heard many times – "How many images do I need to train my classifier?". In the early days I would reply with the technically most correct, but also useless answer of "it depends", but over the last couple of years I've realized that just having a very approximate rule of thumb is useful, so here it is for posterity: You need 1,000 representative images for each class. Like all models, this rule is wrong but sometimes useful. In the rest of this post I'll cover where it came from, why it's wrong, and what it's still good for. The origin of the 1,000-image magic number comes from the original ImageNet classification challenge, where the dataset had 1,000 categories, each with a bit less than 1,000 images for each class (most I looked at had around seven or eight hundred).
This year may not see the technology start to affect our daily lives, but important companies in the supply chain have already started investing in its development. One of the biggest stories to land on PTI's news desk was the joint venture between A.P. Moller - Maersk and IBM, which will provide more efficient and secure methods for conducting global trade using blockchain technology and other cloud-based open source technologies including AI, IoT and analytics. PTI has also been asking some of the top supply chain industry experts what effect AI will have on container shipping. We recently found out from Dr. Yvo Saanen, Commercial Director and Founder of TBA -- an industry-leading consultancy, simulation and software specialist for ports, terminals and warehouses, that the quality of data in the shipping industry will hinder its adoption of AI technologies. But, to find out what may come in 2018, read an extract below from best-selling author and keynote speaker on business, technology and big data, Bernard Marr, who has shared his AI predictions for the year -- first published by Forbes.
Some bemoan how present society has devalued reading skills and comprehension, which doesn't bode well for human civilization as a whole. To add insult to injury, it seems that computers may soon become even better than humans at comprehending what they read. Computers are smart, after all. Their intelligence, however, has limits. Feed them specific, structured data and they'll be able to answer any question you throw at them.
Before the Consumer Electronics Show opens to all its attendees, there's a press day in which many of the bigger manufacturers put on elaborate productions to show off their new products, announce new partnerships, and give us a glimpse into their future tech. It helps set the tone for the show and really get people fired up to hit the floor and check out some new gadgets. This year, LG was one of the first companies on the press conference schedule, and one of its marquis demos was a Hub Robot that embodies the company's digital home assistant, CLOi--pronounced like the human name, Chloe. Instead of making LG look like the future of technology, however, this robot served as a good reminder of just how far we are from the autonomous robot butlers we've been promised. The CLOi bot has a vaguely humanoid face and the kind of cloying appeal you'd expect from a Pixar character.
That's set to change in the next decade. While the service droids will stick around, toiling in their niches, the robots we bring home will be more versatile. They won't be vacuums--they'll use our vacuum cleaners, plus all our other appliances and tools, says Ian Bernstein, co-inventor of the popular Sphero toy robot ball and founder of a startup called Misty Robotics. "Eventually, we should go home and there should be a robot that's already prepared dinner and folded our laundry," Mr. Bernstein says. When we ask what a robot can do, we're really thinking, Can it climb stairs?
A number of weeks ago I solicited feedback from my LinkedIn connections regarding what their typical day in the life of a data scientist consisted of. The response was genuinely overwhelming! Sure, no data scientist role is the same, and that's the reason for the inquiry. So many potential data scientists are interested in knowing what it is that those on the other side keep themselves busy with all day, and so I thought that having a few connections provide their insight might be a useful endeavor. What follows is some of the great feedback I received via email and LinkedIn messages from those who were interested in providing a few paragraphs on their daily professional tasks.
When it comes to Artificial Intelligence and Machine Learning, we seem to be entering a golden age. According to a Research Report by PwC, business leaders believe that AI is going to be fundamental in the future. In fact, 72% termed it a "business advantage." The big players in the tech game like Google and IBM are in the process of creating ground-breaking technologies that will transform life as we know it. The big players in the tech game like Google and IBM are in the process of creating ground-breaking technologies that will transform life as we know it.