A Blog by Jonathan Low

 

Dec 14, 2017

What Goes On At the World's Biggest, Most Prestigious Artificial Intelligence Conference

Methodology debates and corporate recruiting. Not necessarily in that order. JL

Dave Gershgorn reports in Quartz:

Fights between AI’s most visible figures dominated: for engineers to understand more about the techniques they’re using - “Make Machine Learning Rigorous Again,”-  while others argued AI’s accuracy via open-sourced code and borrowed techniques is proof you don’t need to understand every bit of your algorithm. “We had really good ideas in the ’90s. And they are the same ideas that we see now. But now they work.”

Will 5G Networks Be Good Enough For Self-Driving Cars?

Probably not. But the real problem is financial: those who can provide the connectivity have little incentive to do so as the industry is currently configured. JL

Tracy Lindeman reports in Motherboard:

Connected/autonomous vehicles (CAVs) need ubiquitous, reliable, and fast wireless networks to communicate with other vehicles, infrastructure, and devices. The problem is, huge parts of the world have terrible cell and internet coverage and it’s uncertain whether 5G will be enough. Telcos have little financial incentive to deliver stable, low-latency, high-speed coverage. It’s not clear how wireless network providers will make money off of CAVs. Telecoms know how to monetize subscriptions. “It’s hard to monetize a car-to-car network.”

The Reason Google Is Opening an Artificial Intelligence Lab in China

War for talent in an economy where the primary assets are intellectual and human capital.

In AI, Chinese researchers are among the best. There are not enough experts being spawned in the US and Europe, so Google, like every other enterprise interested in this field, has to go to where the leading minds are or risk losing out to competitors. JL


Jonathan Vanian reports in Fortune:

China has become a hotbed of artificial intelligence research in recent years. Many of the research teams responsible for breakthroughs in deep learning that have led to computers automatically recognizing objects in photos are based in China. Google has been trying to attract AI talent in China by promoting its free AI software for developers in the country. Despite the popularity of Google’s free AI software in China, the company faces many challenges attracting AI talent from competing local businesses like Baidu.

Railroads, Telegraphs, Dot.com: Putting the Blockchain Bubble In Context




Why this time probably wont be different. JL


Bill Carmody reports in Inc:

Bubbles don’t kill the technology. The dot com bubble didn’t stop the internet. The railroad bubble wasn’t the end of trains. And the cryptocurrency bubble will not be the end of blockchain technology. After the bubble pops, the underlying infrastructure does not go away. Instead, the building blocks of the infrastructure get really cheap so that other companies can leverage it.

How AI Is Guiding Venture Capital To Startups

Increasing scope, efficiency and productivity. A question is whether this will change the venture capital cliche about investing not in companies but in people. JL

Maija Palmer reports in the Financial Times:

One of the biggest challenges for venture capital companies is finding investment targets. Machine learning and predictive analytics are starting to transform how an investor puts a portfolio together. As well as giving opportunities to a different set of start-ups, machine learning is likely to change the structure of the VC industry. Data scientists and engineers are vital to the business. “What used to be a handcrafted job has become significantly scalable.You become 10 times more productive.”

Meet Your New Boss: An Algorithm

Will algorithmic performance reviews be next? JL

Sam Schechner reports in the Wall Street Journal:

Software is starting to take on management tasks that humans have long handled, such as shepherding strategic projects. Computers may be better suited to some managerial tasks than people are. Humans are susceptible to cognitive traps like confirmation bias. People using intuition tend to make poor decisions but rate their performance more highly. “Managers identify potential, build teams, assign tasks, measure performance and provide feedback. Generally speaking, humans aren’t very good at these tasks,”

Dec 13, 2017

Will B2B Become the Business Tipping Point For Artificial Intelligence?

The business of business is sales. Which means that if AI is to optimize its promise, it will demonstrate that it can turbocharge the selling process. JL

Pete Eppele reports in Forbes:

AI has seen cycles of hype and disillusionment. This cycle feels different. Practical business applications that drive measurable value will grow. Critical inflection points will precipitate the widespread acceptance of AI . First, the build-versus-buy gap will grow to a chasm. Second, a C-suite customer focus will yield high returns. Third, AI will supercharge all sales channels, from individual reps to ecommerce. AI will maximize customer potential while putting more salespeople to work.