Five Hot AI Startups Step into Spotlight at GTC Europe Inception Awards

Five hot #AI startups step into the spotlight at the #GTC17EU Inception Awards:

  • Then we gave one of them — Gamaya, a 20-person startup harnessing deep learning to help farms improve their productivity and sustainability — a new DGX Station in front of a room packed with more than 160 investors, entrepreneurs and industry observers.
  • The event’s contenders were selected from among the 700 European startups participating in our Inception program, which accelerates the development of startups involved in AI and deep learning.
  • After looking at an initial round of 25 startups, our judges chose companies we believe to be the five hottest in Europe to tell their stories.
  • Besides our winner Gamaya, the startups included presentations from: – – The Inception Awards continue the series of events we’ve held in Silicon Valley and China in conjunction with our GPU Technology Conference world tour.
  • Our Inception virtual accelerator program supports more than 1,900 AI startups with GPUs, deep learning expertise and other resources to help them be successful.

We brought five of the hottest startups in Europe and put them in front of a panel of some of tech’s savviest players at GTC Europe in Munich Tuesday.
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Why Google and Facebook are the best AI-first organizations // FABERNOVEL

Interesting look at how Google and Facebook are building AI-first organizations  #AI

  • We studied carefully the 2 most advanced organizations that embody this principle, Google and Facebook, to understand how they are structured.
  • Whatever the company you belong to, there are a few lessons to be drawn from these 2 exemplars to design your own AI-first organization: AI-first organizations are complex!
  • AI-first organizations are embodied through various means: not only teams, but also internal software platforms, open-source side projects, and the final services delivered.
  • In AI-first organizations, the most technical experts are not only tasked with creating breakthrough research projects or new technologies; they must also lead internal training efforts.
  • AI-first organizations are inherently agile: in the aggregate, their goal is to maximize both the number of experiments leveraging AI, as well as the speed of deployment and capacity of scaling the successful ones.

At the last Google I/O conference, Google’s CEO Sundar Pichai emphasized the ongoing shift from a mobile-first to an AI-first world. What does it mean in
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This dystopian device warns you when AI is trying to impersonate actual humans

This Dystopian Device Warns You When #AI Is Trying to Impersonate Actual Humans

  • The wearable prototype device is designed to identify synthetic speech and alert the user that the voice they’re listening doesn’t belong to a flesh-and-blood individual.
  • As artificial intelligence (AI) and robotic technology rapidly evolve, we’re facing an uncertain future where machines can seemingly do all sorts of things better than people can – from mastering games to working our jobs, and even making new, more powerful forms of AI.
  • A team at Australian creative technology agency DT trained its AI up on a database of synthetic voices, teaching the offline network to recognise artificial speech patterns.
  • If the AI detects an actual human voice (code green), all is fine:

    But if the system picks up on synthetic speech, it has a unique way of subtly letting the human know that they’re talking to a digital clone.

  • “We wanted the device to give the wearer a unique sensation that matched what they were experiencing when a synthetic voice is detected,” the team explains on DT’s R&D blog.

Meet the Anti-AI AI.
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‘Eureka moment’: AI software programmed to spot skin cancer by checking pics of moles — RT News

‘Eureka moment’: #AI software programmed to spot skin cancer by checking pics of moles

  • Scientists at Stanford University have taught an AI to diagnose early signs of skin cancer by feeding tens of thousands of images to its recognition database.
  • Researchers are working on finding other medical uses for deep learning such as spotting diabetic retinopathy or other forms of cancer.
  • Deep learning is a series of algorithms which mimic human neural networks, allowing the AI to detect patterns in images.
  • Diagnosis is usually made by spotting the telltale signs by eye, which is where this software may become useful.
  • The researchers tested the AI against 130,000 images, comparing the results to diagnoses of 21 clinicians.


Scientists at Stanford University have taught an AI to diagnose early signs of skin cancer by feeding tens of thousands of images to its recognition database. They hope the AI would eventually become an effective cancer detection tool outside of clinics.

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Human or AI: Can You Tell Who Composed This Music?

Human or #AI: can you tell who composed this music?

  • The results showed that more than half the listeners attributed DeepBach-generated harmonies to Bach, while music by Bach was correctly identified by 75 percent of the listeners. “
  • Music isn’t the only field where AI algorithms have performed considerably well, even outperforming actual people in some cases.
  • Interestingly enough, Bach’s music makes it into “Westworld,” a TV series that exploits the topic of AI.
  • Baroque composer Johann Sebastian Bach is known to have written many chorale cantatas, polyphonic hymns based on Lutheran texts.
  • Music is mathematical, and composers like Bach often made music that followed a defined, step-like flow that is almost algorithmic.

More than 50% of listeners were convinced that the harmonies created by neural network DeepBach were actually written by famous composer Johann Sebastian Bach. DeepBach is one of many examples of an AI system performing as well as or even better than its human counterparts, a sign that the technology is significantly improving.
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Your People’s Brains Need Face Time

#fintech #insurtech remember

#AI, #robots & #chatbots? Brains Need Face Time!

#leadership

  • Team members see how much the company is investing in bringing them together.”
  • Team members are reminded of their colleagues’ humanity and learn to respect and better understand each other in ways that don’t materialize when they only engage remotely.
  • A team becomes more productive and cohesive as a result.
  • The technology that supports virtual meetings and dispersed teams is only getting better and less expensive.
  • A couple of years back, one of the teams decided to meet in-person about halfway through.

Dispersed teams trust more and function better when exposed to their colleagues’ non-verbal cues, best communicated in-person.
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