Latest AI Articles:

1 - Diffbot Raises $10M Series A to Become Leading Arms Dealer in Coming AI Wars

The Internet of Things (IoT) has the potential to fall into the general pit of buzzword-vagueness. Artificial intelligence (AI) often falls into the same trap, particularly with the advent of new terms such as "machine learning," "deep learning," "genetic algorithms," and more.

Despite the progress made in artificial intelligence over the past few years, deep learning software still lags far behind the pattern recognition and learning capabilities of the mammalian mind. Where a human might be able to recognize an apple after seeing just a couple apples, even the most sophisticated deep learning software has to review hundreds of thousands of apples to identify one.

At the heart of our present day sharing economy is the often lauded, sometimes corrupted, and occasionally controversial open source model. Though the open source model has its roots in the early days of automobile development, our Internet age has proved an ideal medium for free licensing and distribution.
 
The world’s biggest names in technology – particularly those in Silicon Valley – have released their artificial intelligence technology via the open source model over the past few months in a domino effect that has made some of the most sophisticated AI programs available to anyone with Internet connection. In huge maneuvers, Google, Facebook, Microsoft, and China’s search engine giant Baidu have taken deep learning even deeper.
In November of last year, Google open sourced the software library for TensorFlow, the tech giant’s perceptual and language comprehension program. Though TensorFlow wasn’t the first open source AI software out there – software such as Torch, Caffe, and Theano – it is widely regarded as some of the most advanced AI algorithms in the world. Thus Google’s move to make TesorFlow open source marked an unparalleled step forward, which its competition couldn't resist but to follow.

Over the last five months, we've combined a mix of interviews and surveys with some of our most experienced artificial intelligence guests - including computer science PhDs from Stanford, Georgia Tech, and more - as well as some of the most renowned AGI researchers in the world.

Episode Summary: Ever had the perfect book recommended to you by Amazon or gave a pleasantly-surprised thumbs up for a song selected for you by Pandora? Both services are powered by recommendation engines, which are gaining steam int he commercial space. In this episode, we speak with Entrepreneur Raefer Gabriel, who works for Delvv on the commercial applications of recommendation engines. We talk about how this technology works, and how it comes to learn from reviews, ratings, and consumer interactions. Gabriel also gives perspective on how these engines might be enhanced and applied in the future, a good topic for those of you in the startup world.

White collar professions were once considered safe from automation. It was blue collar work such as labor and manufacturing jobs that appeared at risk of becoming redundant in the wake of advancing technologies. But according to the Word Economic Fund – who held a conference last week in in Davos Switzerland – white collar work is not so secure as it seemed. AI systems continue to advance and challenge the status quo.

Episode Summary: The beauty of a platform like eBay is that you can set a price that you’re willing to spend and let eBay do the bidding long after you’ve left the site. What if, in similar fashion, your washing machine could turn on and serve up clean clothes once it had found the cheapest rate and time of day by autonomously communicating with local electricity providers?

The "fourth industrial revolution" is upon us and, according to the World Economic Fund, it is set to drastically disrupt business modes, labour markets, and economies across the world. In fact, in a report released this week, the Swiss foundation gave a conservative estimate of 7.1 million jobs that could vanish due to redundancy and automation by 2020. Some 2.1 million jobs will be created and marginally offset that loss – but the 5 million remaining, mostly white collar jobs, will see themselves performed by one or more  machine.
 
Where previous industrial revolutions were powered by tools that workers could control, the current revolution is lead by machines which may well control themselves. The WEF lists artificial intelligence and machine-learning among the most disruptive technologies to date, predicting that the advancements in these fields will cause “enormous change…in the skill sets needed to thrive.”
The report comes just one day before the WEF’s annual forum in Davos, Switzerland – a forum to bring over 2,500 business leaders, governmental figures, and members of society together to discuss the state of the global economy. This year, the focus will be on jobs, with a particular emphasis on the effects of potential but widespread automation.
To formulate their report, the WEF surveyed held a broad survey representing 65 percent of the global workforce, including senior executives from 350 companies from nine industries and 15 economies.
The report found that healthcare, energy, financial services, and investors will take the biggest hit from automation. We’ve seen how AI and robots already perform as surgeons and caregivers. Earlier this year, Financial Times and the BBC reported how AI programs are transforming the financial industry.

Episode Summary: Over the last decade, many first-world militaries have developed, and in some cases deployed, autonomous “killer”  robots. Some proponents believe that such robots will save human lives, but another side believes that an accidental arms race of this type would yield long-term detriments that outweigh any good. University of Sheffield’s Dr. Noel Sharkey stands by the latter argument.