Industry News

Industry News

Machine learning AI News, covering Facebook raids universities for AI talent as their AI Go goes Open Source, Welcome to the AI gold rush, Even Human AI fails, the $100M Startup to Take on Alexa and Google Assistant, and “How to code movie recommendation ML.

AI & Machine Learning News. 07, May 2018

Start your week
with a little AI Fun!


Inspired by DeepMind, Facebook Open Sources it’s own Go Beating Algorithm

The game Go has become quite the hit lately in the machine learning community ever since DeepMind unveiled it’s AlphaGo algorithm. It was the first machine led effort that beat a Go world champion. Since then, a lot of data scientists and researchers have dedicated themselves to understanding how to better DeepMind’s algorithm. The latest effort was by Facebook’s Research team.

  • Facebook has presented an open sourced ELF OpenGo, a bot that plays the popular Go game
  • ELF OpenGo won 198 of 200 games against the strongest publicly available bot; it also beat humans 14 out of 14 times!
  • The code links and other resources are included in the article

2018-05-05 10:53:30+05:30 Read the full story.
CloudQuant Thoughts: Be careful, it is watching your game and working out if you also like My Little Pony.

Welcome to the AI gold rush!

Who Is Going To Make Money In AI? Part I – Towards Data Science
We are currently experiencing another gold rush, in AI. Billions are being invested in AI startups across every imaginable industry and business function. Google, Amazon, Microsoft and IBM are in a heavyweight fight investing over $20 billion in AI in 2016. Corporates are scrambling to ensure they realise the productivity benefits of AI ahead of their competitors while looking over their shoulders at the startups. China is putting its considerable weight behind AI and the European Union is talking about a $22 billion AI investment as it fears losing ground to China and the US.
2018-05-06 12:26:26.967000+00:00 Read the full story.
CloudQuant Thoughts: A great article, and obviously we think the money is to be made in the Finance vertical. Try your hand at and soon with CloudQuant AI.

When Even a Human is Not Good Enough as Artificial Intelligence

During a study on machine learning and applications research, I faked being an AI like the Mechanical Turk chess-playing machines of times gone by. During the post-interviews I heard the most bizarre statement from multiple attendees: “The AI made a lot of mistakes.”. This post is about people’s bias against AI’s performance and how realistic our expectations are from AI systems.
2018-05-06 12:54:40.778000+00:00 Read the full story.
CloudQuant Thoughts: This is interesting, that most people’s expectations for AI is 100% accuracy, yet when a human “pretends” to be AI we judge its performance harshly. What hope is there for our algo AI?

Facebook Adds A.I. Labs in Seattle and Pittsburgh, Pressuring Local Universities

Facebook is opening new A.I. labs in Seattle and Pittsburgh, after hiring three A.I. and robotics professors from the University of Washington and Carnegie Mellon University. The company hopes these seasoned researchers will help recruit and train other A.I. experts in the two cities, Mike Schroepfer, Facebook’s chief technology officer, said in an interview. As it builds these labs, Facebook is adding to pressure on universities and nonprofit A.I. research operations, which are already struggling to retain professors and other employees.
The expansion is a blow for Carnegie Mellon, in particular. In 2015, Uber hired 40 researchers and technical engineers from the university’s robotics lab to staff a self-driving car operation in Pittsburgh. And The Wall Street Journal reported this week that JPMorgan Chase had hired Manuela Veloso, Carnegie Mellon’s head of so-called machine learning technology, to oversee its artificial intelligence operation. “It is worrisome that they are eating the seed corn,” said Dan Weld, a computer science professor at the University of Washington. “If we lose all our faculty, it will be hard to keep preparing the next generation of researchers.”
2018-05-04 00:00:00 Read the full story.
2018-05-05 17:05:57-07:00 Also on Geekwire.
CloudQuant Thoughts: It is obvious that there are not many AI specialists out there if the behemoths have taken to raiding the Universities.

$100M Startup to Take on Alexa, Google Assistant

SoundHound Inc., a The Santa Clara, California-based startup that works with customers across automotive, Internet of Things (IoT), consumer and enterprise service industries that want to create their own artificial intelligence-driven virtual assistants, announced the close of a $100 million mega-round,led by Chinese Internet giant Tencent,  to accelerate the global expansion of its platform that rivals Inc.’s (AMZN) Alexa and Alphabet Inc.’s (GOOGL) Google Assistant.
2018-05-07 04:00:00-06:00 Read the full story.
CloudQuant Thoughts: Just when you thought Google, Amazon, and Apple had it all tied up, it turns out that car manufacturers don’t what their customers calling their cars “Alexa” or having them permanently logged into to Google, Amazon or Apple.

How to implement four different movie recommendation approaches

“What movie should I watch this evening?”
Have you ever had to answer this question at least once when you came home from work? As for me — yes, and more than once. From Netflix to Hulu, the need to build robust movie recommendation systems is extremely important given the huge demand for personalized content of modern consumers.

  1. Content-Based Filtering
  2. Memory-Based Collaborative Filtering
  3. Model-Based Collaborative Filtering
  4. Deep Learning / Neural Network

2018-05-04 13:28:53.906000+00:00 Read the full story.
CloudQuant Thoughts: A nice demonstration with code…

Below the Fold…

Fed’s Quarles says paying ‘a lot’ of attention to spread of machine learning in finance

Federal Reserve vice chair for supervision Randal Quarles said the central bank is in the early stages of studying how the expanding use of machine learning in the financial sector may change its regulatory approach, but that so far it fits within the existing regulatory framework.
“We are paying a lot of attention … within our existing framework. To the extent you have a machine learning tool that is interacting with customers we want to make sure that the traditional protections are being complied with,” Quarles said.
2018-05-06 00:00:00 Read the full story.

SEC’s Bauguess on the role of machine readability in an AI world

This morning, I want to share with you some thoughts in this area, particularly as they relate to the role of regulatory data.

  • Myth #1: Electronic access is equivalent to machine readability.
  • Myth #2: The Commission alone develops the reporting standards incorporated in its rules.
  • Myth #3: Retail investors don’t need machine-readable data.
  • Myth #4: Requiring machine-readable reporting standards ensures high-quality data.
  • Myth #5: We don’t need the public’s views any more.

I started these remarks by acknowledging that what has fueled the machine learning revolution is data.
And not just any data, but data designed to answer questions that market participants ask.
Sophisticated algorithms depend on this data being of high quality and being machine readable.
When applied to the emerging fields of SupTech and RegTech, there is tremendous potential for enhanced regulatory compliance.
The enhancements can come at a lower cost to registrants.
2018-05-03 18:48:00 Read the full story.

Fast Near-Duplicate Image Search using Locality Sensitive Hashing

A quick 5-part tutorial on how deep learning combined with efficient approximate nearest neighbor queries can be used to perform fast semantic similarity searches in huge collections.
If you have some education in Machine Learning, the name Nearest Neighbor probably reminds you of the k-nearest neighbors algorithm. It is a very simple algorithm with seemingly no “learning” actually involved: The kNN rule simply classifies each unlabeled example by the majority label among its k-nearest neighbors in the training set.
2018-05-05 12:16:22.405000+00:00 Read the full story.

Essentials of Deep Learning: Introduction to Unsupervised Deep Learning (with Python codes)

I am planning to write a series of articles focused on Unsupervised Deep Learning applications. This article specifically aims to give you an intuitive introduction to what the topic entails, along with an application of a real life problem. In the next few articles, I will focus more on the internal workings of the techniques involved in deep learning.
Note – This article assumes a basic knowledge of Deep Learning and Machine learning concepts.
2018-05-06 22:07:12+05:30 Read the full story.

Python or R? Hadley Wickham and Wes McKinney are Building Platform Independent Libraries!

Ursa Labs is aiming to create libraries that will work on multiple programming languages, including R and Python
It has been founded by pandas creator Wes McKinney. Hadley Wickham is the technical advisor
2018-05-07 11:38:21+05:30 Read the full story.

Inside Multimodal Neural Network Architecture That Has The Power To “Learn It All”

Multimodal machine learning is a multi-disciplinary research field that addresses some of the original goals of artificial intelligence by integrating and modelling multiple communicative modalities, including linguistic, acoustic and visual messages. It is often referred to as building models that can process information from multiple sources.
2018-05-05 06:52:30+00:00 Read the full story.

Forget AGI (Artificial General Intelligence), let’s build really useful AI tools

The biggest opportunities in machine learning (ML) today lie not in cracking the next big nut on the path to artificial general intelligence (AGI), but in opening up existing machine learning techniques to more businesses and making them more usable. The tech giants already know this and are investing in democratizing AI to make tools and services more widely available, but the user experience (UX) of machine learning is still overlooked.
2018-05-06 00:00:00 Read the full story.

Lobe is an Automated Deep Learning Tool for People who don’t know Programming

Lobe is a visual drag-and-drop tool that automated deep learning; no coding necessary! It lets you build custom DL models, tune them, and deploy them to your application. All the model training is done on the cloud so your machine’s performance is not impacted.
2018-05-04 10:51:30+05:30 Read the full story.

EPAM Platform Now Uses ML to Find Data Anomalies

The addition of machine learning through neural networks significantly improves the fidelity of the information over time, allowing users to find hidden patterns, trends and anomalies, the company said. InfoNgen users are able to quickly find, analyze and share business information to speed decision-making and remain competitive.
Nearly 80 percent of new data is unstructured, making it the fastest growing form of data to be stored, and companies not taking advantage of it are missing large amounts of relevant information.
2018-05-04 00:00:00 Read the full story.

Can a computer name lipstick colors? – Towards Data Science

Based on my hunch that these lipstick names follow a certain unspoken formula, I wanted to know if a computer could learn their patterns and produce new ones. I took hundreds of lip color shades (including balms, gloss, liners and lipsticks) from the Sephora website, as well as additional shades from drugstore standbys like Revlon. I fed this dataset to a neural network, a deep learning model that learns the structure of text and can produce its own rendition of whatever material it was trained on.
2018-05-06 04:03:39.935000+00:00 Read the full story.

How Artificial Intelligence Is Influencing Customer Experience Today

Artificial intelligence is being accepted now on a global scale. According to a report from IDC, the global investment in artificial intelligence is only increasing with time. And in the coming years too, this is going to increase. The global investment is directly proportional to the faith in this technology. And with the passing time, the increasing faith in artificial intelligence has also become possible because of the ongoing contemporary innovations in the cloud. Artificial intelligence will deliver a $1.1 trillion boost to global business revenue and create 800 thousand new jobs in the next five years.
2018-05-02 09:14:06-04:00 Read the full story.

Web Scraping, Regular Expressions, and Data Visualization: Doing it all in Python

A Small Real-World Project for Learning 3 Invaluable Skills

As with most interesting projects, this one started with a simple question asked half-seriously: how much tuition do I pay for five minutes of my college president’s time? After a chance pleasant discussion with the president of my school (CWRU), I wondered just how much my conversation had cost me.

2018-05-04 13:28:53.906000+00:00 Read the full story.

Barclays Upgrades Nvidia Stock on AI, Gaming Potential

Technology name Nvidia Corporation (NASDAQ:NVDA) is trading higher this morning, after getting upgraded to “overweight” from “equal weight” at Barclays, which also lifted its price target to $280 from $265. The brokerage firm said it believes the chip producer will benefit from the next wave in artificial intelligence, while also predicting strength in the company’s gaming business. At last glance, NVDA stock was up 0.7%, to trade at $227.80.
2018-05-03 00:00:00 Read the full story.

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