The AI revolution: How artificial intelligence can boost your bottom line (VB Live)

The AI revolution: How artificial intelligence can boost your bottom line (VB Live)

  • Anticipate changes in the AI landscape and plan
  • We’ll go over the AI landscape and how the top players in the industry are using AI to better their bottom line.
  • The AI revolution: How artificial intelligence can boost your bottom line (VB Live)
  • AI now plays a pivotal role in so many industries, it simply cannot be ignored – those who do will simply become charmingly anachronistic.
  • AI is part of the mainstream and will only get bigger, as technology continues to improve in mimicking human behaviour, and the big players in the tech industry are taking notice.

Read the full article, click here.


@VentureBeat: “The AI revolution: How artificial intelligence can boost your bottom line (VB Live)”


AI now plays a pivotal role in so many industries, it simply cannot be ignored — those who do will simply become charmingly anachronistic. Join this essential VB Live event to understand the current AI landscape and how those who are winning at it, are winning big.


The AI revolution: How artificial intelligence can boost your bottom line (VB Live)

Will Facebook Messenger Chat Bots Replace Humans?

Will Facebook Messenger Chat Bots Replace Humans?  #AI #socialmedia #tech

  • But even with the team, KLM relies on chat bots to help customers with basic tasks.
  • Covering the world of social media
  • Wim ter Haar does not believe that these chat bots will replace human customer service agents.
  • According to TechCrunch , as many as 10,000 companies are developing chat bots for Facebook Messenger.
  • If social customer care is placing a burden on your business, it is something to consider.

Read the full article, click here.


@zaibatsu: “Will Facebook Messenger Chat Bots Replace Humans? #AI #socialmedia #tech”


As a natural response to Facebook’s push to get more pages to use Messenger as a platform, brands are developing chat bots to handle customer service. How well is this working?


Will Facebook Messenger Chat Bots Replace Humans?

Google Deep Learning Founder says Tesla’s Autopilot system is ‘irresponsible’

Google Deep Learning Founder says Tesla’s Autopilot system is ‘irresponsible’

  • Tesla will prioritize Model 3 reservations for current Tesla owners over non-owners
  • It doesn’t seem fair for Ng to ask not to ship the Autopilot, which is extremely useful to tens of thousands of Tesla owners, just because a few of them are abusing the system.
  • Following a few recent accidents in the past weeks, Tesla received some criticisms over its approach on releasing semi-autonomous features.
  • One of the most severe criticism came from renowned scientist Andrew NG who said that it was plainly “irresponsible” for Tesla to ship the Autopilot.
  • It’s still difficult to agree with Ng’s claim of creating a “false sense of safety” when Tesla asks drivers to “prepare to take immediate corrective action” and even recommend to keep their hands on the wheel – it doesn’t do much to create a sense of safety.

Read the full article, click here.


@ElectrekCo: “Google Deep Learning Founder says Tesla’s Autopilot system is ‘irresponsible’”


Tesla Model 3 exclusive leaked specs: 0-60 under 4 sec fast and 300+ mile range options (Update: Base 6 sec 0-60 and 215 mile range)


Google Deep Learning Founder says Tesla’s Autopilot system is ‘irresponsible’

GitHub

Very interesting code, lda2vec tools for interpreting natural language  #machinelearning #NLP

  • We build a model that builds both word and document topics, makes them interpreable, makes topics over clients, times, and documents, and makes them supervised topics.
  • lda2vec also yields topics over clients.
  • lda2vec the topics can be ‘supervised’ and forced to predict another target.
  • It’s research software, and we’ve tried to make it simple to modify lda2vec and to play around with your own custom topic models.
  • LDA on the other hand is quite interpretable by humans, but doesn’t model local word relationships like word2vec.

Read the full article, click here.


@andradeandrey: “Very interesting code, lda2vec tools for interpreting natural language #machinelearning #NLP”


Contribute to lda2vec development by creating an account on GitHub.


GitHub

US government “Preparing for the Future of Artificial Intelligence”

US government

  • Researchers disagree on when artificial intelligence that displays something like human understanding might arrive.
  • “Thinking in Silicon” – Fascinating article on upcoming AI trends and computing
  • Automation likely to lead to a society that will rely on a basic income structure – Tests are underway
  • Artificial intelligence systems are beginning to surpass human creativity
  • Read more about: Artificial Intelligence Politics Good Thinking Future Trends Robotics Basic Income Automation Singularity

Read the full article, click here.


@FuturistechInfo: “US government “Preparing for the Future of Artificial Intelligence””


Researchers disagree on when artificial intelligence that displays something like human understanding might arrive. But the Obama administration isn’t waiting to find out. The White House says the government needs to start thinking about how to r…


US government “Preparing for the Future of Artificial Intelligence”

International Conference on Learning Representations (ICLR) 2016, San Juan

#ICLR2016 talks and video lectures  #MachineLearning #DeepLearning

  • Guaranteed Non-convex Learning Algorithms through Tensor Factorization Guaranteed Non-convex Learning Algorithms through Tensor Factorization
  • Incorporating Structure in Deep Learning Incorporating Structure in Deep Learning
  • We take a broad view of the field, and include in it topics such as deep learning and feature learning, metric learning, kernel learning, compositional models, non-linear structured prediction, and issues regarding non-convex optimization.
  • The rapidly developing field of representation learning is concerned with questions surrounding how we can best learn meaningful and useful representations of data.
  • Deep Compression: Compressing Deep Neural Networks with Pruning, Trained Quantization and Huffman Coding Deep Compression: Compressing Deep Neural Networks with Pruning, Trained Quantization …

Read the full article, click here.


@kdnuggets: “#ICLR2016 talks and video lectures #MachineLearning #DeepLearning”


ICLR is an annual conference sponsored by the Computational and Biological Learning Society. It is well understood that the performance of machine learning methods is heavily dependent on the choice of data representation (or features) on which they are applied. The rapidly developing field of representation learning is concerned with questions surrounding how we can best learn meaningful and useful representations of data. We take a broad view of the field, and include in it topics such as deep learning and feature learning, metric learning, kernel learning, compositional models, non-linear structured prediction, and issues regarding non-convex optimization. Despite the importance of representation learning to machine learning and to application areas such as vision, speech, audio and NLP, there was no venue for researchers who share a common interest in this topic. The goal of ICLR has been to help fill this void.


International Conference on Learning Representations (ICLR) 2016, San Juan

IBM, the “cognitive computing” company, sees huge upside in cognitive computing

G.Rometty sees trillions in opportunity through #MachineLearning in next 10 years  #SD216 #AI

  • Through Machine Learning, IBM Braintrust Sees Better Days Ahead
  • IBM ibm chief executive Ginni Rometty said the company sees a huge opportunity-$2 trillion worth-in this market over the next decade.
  • Rometty stressed, more than once, that IBM is now a “cognitive computing and cloud company.”
  • Market data provided by Interactive Data .
  • IBM, he noted, also predicted a huge market in business process outsourcing a few years back, and that market didn’t meet expectations.

Read the full article, click here.


@SeverineLienard: “G.Rometty sees trillions in opportunity through #MachineLearning in next 10 years #SD216 #AI”


IBM CEO Ginni Rometty claims a $2 trillion opportunity in machine learning, over the next ten years.


IBM, the “cognitive computing” company, sees huge upside in cognitive computing