Industry News

Industry News

Predicting the sale date of cars, Robots building flatpack furniture, Westworld required viewing, AI’s value in healthcare, NLP example, and the Tech Giants provide courses to find and train the next generation of AI/ML experts.

AI & Machine Learning News. 22, April 2018 is using Machine Learning to Predict the Sales of Cars has built a machine learning model to help buyers determine when and how to act on a purchase.
The technology is called “Hot Car” and has been built on over 20 years of data using over 50 factors. The initial testing resulted in a double-digit increase in sales
2018-04-19 11:10:02+05:30
CloudQuant Thoughts: Double-digit growth is an extremely impressive ROI for AI/ML.

The Rise of AI Continues – Robots have Mastered the Task of Assembling Furniture

Researchers from Singapore have created 2 robotic arms that can assemble an IKEA chair. It took them 19 minutes and 20 seconds to do it; humans take 15 minutes on average. Science Magazine also has an article on these robots. Their staff tried the same challenge and only beat the robots by, on average, 50 seconds.
It took multiple attempts and a lot of training data to make the AI workable. Check out the video below to see this technology in action.

2018-04-21 11:21:31+05:30
2018-04-18 13:30:44-04:00
CloudQuant Thoughts: White Glove delivery and assembly of furniture is the golden egg of the furniture business. It costs too much and is too variable (ability of the person assembling the furniture, quality of the delivered goods – faulty/parts missing) . We can easily forsee trained robots delivering and assembling the furniture for you.

Deep learning in your browser: Use your webcam and Tensorflow.js to detect objects in real time.

Tensorflow.js is a new deep learning library that runs right in your browser. Being a machine learning and Javascript enthusiast, I immediately started working on an object detection library using Tensorflow.js after it was released.
Here, I’ll walk through how I got started. You can check out the live demo here.
2018-04-20 19:59:54.715000+00:00—2——0—————-
CloudQuant Thoughts: Sounds great, needs access to your webcam and, unfortunately, didn’t work for me!

AI Weekly: Why all developers should watch ‘Westworld’

Last year, futurist David Brin predicted a “robot empathy crisis” would come in the years ahead, spurring debate among humans about the right way to treat a robot that looks and acts like a human.
It may be easy for some to dismiss ideas like robot personhood or a robot empathy crisis, but it’s a worthwhile discussion, and Westworld is one of the only shows on television that persistently explores how human treatment of machines could change our humanity or reflect our worst demons. After all, slavery doesn’t just affect the enslaved — it also corrupts those who treat them as less than human.
2018-04-20 00:00:00
CloudQuant Thoughts: There are already signs that the subservient style of these personal assistants with female voices is affecting our childrens politeness. See another great NPR article (Alexa – are you safe for my kids).

The 3 most valuable applications of AI in health care

recent report from Accenture analyzed the “near-term value” of AI applications in health care to determine how the potential impact of the technology stacks up against the upfront costs of implementation. Results from the report estimated that AI applications in health care could save up to $150 billion annually for the U.S. health care economy by 2026.

  1. Robot-assisted surgery: Estimated value of $40 billion
  2. Virtual nursing assistants: Estimated value of $20 billion
  3. Administrative workflow assistance: Estimated value of $18 billion

An example of this comes from Nuance. The company provides AI-powered solutions that rely on machine learning to help health-care providers cut documentation time and improve reporting quality. Computer-assisted physician documentation (CAPD) like this provides real-time clinical documentation guidance that helps providers ensure their patients receive an accurate clinical history and consistent recommendations.
2018-04-22 00:00:00
CloudQuant Thoughts: As traders, we have seen health care stocks soar as their profits soar and our Insurance deductions soar. It would be great to see this vicious circle interrupted by AI and ML.

Using Natural Language Processing To Check Word Frequency In ‘The Adventures of Sherlock Holmes’

Natural Language Processing is one of the most commonly used technique which is implemented in machine learning applications — given the wide range of analysis, extraction, processing and visualising tasks that it can perform. In this article, you will learn how to implement all of these aspects and present your project. The primary goal of this project is to tokenize the textual content, remove the stop words and find the high frequency words. We shall implement this in Python 3.6.4.
To start with, we shall look into the libraries that we are going to use:

  • Beautifulsoup: To scrape the data from the HTML of a website and it also helps to process only the text from these HTML codes
  • Regular Expressions: Also known as Regex. It will convert the noise data containing special characters and carry the conversion of uppercase to lowercase characters
  • NLTK (Natural Language Toolkit): For the tokenization of the sentences into a list of words

We are using the eBook “The Adventures of Sherlock Holmes by Sir Arthur Conan Doyle”, which is available here.
2018-04-19 12:45:28+00:00
CloudQuant Thoughts: A great starter article. Being able to read and analyze text/news is a huge benefit for trading stocks. Of course you could always just use the sentiment datasets we have available over at which group and categorize earnings, news, twitter and stocktwits tweets for you as a single “sentiment” number.

9 AI And ML Courses Offered By Tech Giants Which Will Boost Your Career

By now almost every tech company has realised that the world needs more artificial intelligence and machine learning experts. There are only 10,000 people in the world right now with the education, experience and talent needed to develop these AI technologies. This acute lack of skill set is hindering digital transformation at enterprises across the globe.
To meet this talent shortage, the tech giants have now become more committed to making ML more accessible to students and developers by offering online courses.

  1. Google – Learn with Google AI
  2. Google – ML Crash Course
  3. Microsoft – Microsoft Professional Program For AI
  4. Amazon – Introduction to ML
  5. Amazon – Deep Learning on AWS
  6. NVIDIA – Deep Learning Course
  7. Baidu –
  8. Intel – Intel Student Ambassador Program for AI
  9. Uber – Uber AI Residency

2018-04-20 07:00:39+00:00
CloudQuant Thoughts: This is a hot career and there many opportunities out there… Including writing Automated Trading Algorithms at See more on the demand for data scientists in our new section “below the fold”…

Below the fold….


A.I. Researchers Are Making More Than $1 Million, Even at a Nonprofit

One of the poorest-kept secrets in Silicon Valley has been the huge salaries and bonuses that experts in artificial intelligence can command. Now, a little-noticed tax filing by a non-profit research lab called OpenAI has made some of those eye-popping figures public.

OpenAI paid its top researcher, Ilya Sutskever, more than $1.9 million in 2016. It paid another leading researcher, Ian Goodfellow, more than $800,000 — even though he was not hired until March of that year. Both were recruited from Google.

Salaries for top A.I. researchers have skyrocketed because there are not many people who understand the technology and thousands of companies want to work with it.

2018-04-19 00:00:00

Why the transportation sector needs data scientists

The transportation industry is ripe for advancement. With the addition of Internet of Things (IoT) technologies, the industry might not be recognizable in 10 or 20 years. More connectivity means fully optimized operations and manufacturing, decreased downtime and accidents, and — what everyone is waiting for — driverless vehicles and ships.
CIO magazine identified the top 10 skills necessary to tackle IoT as machine learning, AutoCAD, Node.js, security infrastructure, security engineering, big data, GPS development, electrical engineering, circuit design, and microcontroller programming.
While one data scientist will not have all of those skills, many choose to specialize in some of them, especially machine learning. And since the basis of IoT is using massive amounts of data, there absolutely must be a data person on the IoT team.
2018-04-20 00:00:00

4 Ways to fail a Data scientist job interview

‘Data Scientist’ might well be the sexiest job of the century. But hiring one is anything but that. Actually, it can be excruciatingly painful for companies. It’s an equally big deal for aspirants to bag that perfect offer in core data science, one which is not just a glossed-up, namesake role.
While machine learning is tough, training a human who can make machines learn can be tougher. One evolves through various incremental stages of expertise to become a productive data scientist.
For companies trying to identify one, it’s like finding a needle in the haystack. After years of hiring data scientists at Gramener, I’ve seen some conspicuously recurring patterns of skill gaps in the market. While there are hundreds of ways to fail an interview, these can be isolated into 4 broad paths.
1. Window dressing the CV with machine learning buzzwords
2. Reducing model-building to just making library calls
3. Lacking the fundamentals essential for data analysis
4. Inability to apply analytics to solve business problems

2018-04-20 13:02:22.165000+00:00


Job Application Guide for Data Science Students

In the past few years, I have met up with a lot of employers and conducted interviews for training programs. After all the conversations and interviews and seeing the end results, I thought I would share more on how to prepare for your resume and even prepare for the interviews for a data science role. Most of the tips are for people who want to enter into the data science profession with a “green” background. I cannot promise results but I hope it can help those who are passionate about data science.
2018-04-22 03:26:34.217000+00:00—4——0—————-

Are High Level APIs Simplifying ML To An Elementary Level?

High-level APIs in machine learning are the best way to accelerate data science workflows. With a slew of tools available in the market, startups and enterprises are deploying machine learning tools to automate repetitive tasks. ML software libraries with APIs such as TensorFlow, Keras and Scikit have simplified the implementation to a great extent by providing more flexibility with user experience as well as the ease of working with ML applications.
But data science practitioners have for long argued the effectiveness of APIs that have dumbed down computer science for beginners. While APIs increases the productivity by testing a bunch of models and cranking out the best model, it can also promote the black box problem as practitioner’s lack basic understanding of how algorithms work.
In the words of Matthew Mayo, ML researcher, implementing ML models is easier with libraries but does it go too far up in terms of abstraction for beginners to truly understand the theory of the algorithms?
2018-04-20 07:14:59+00:00

How I trained an AI to detect satire in under an hour

I decided to give myself the challenge of seeing if I could teach a machine learning model to detect the difference between Onion articles (and other satire) and real news articles in less than an hour. These were my results:
The first step to solving this problem was gathering the training data. This is by far the most difficult part. And there are a number of ways you might accomplish this. One way is to try scraping websites. I’m not really good enough at computers to figure out how to scrape websites, so I decided to gather the data manually (cringe). …

In about 1 second, Classificationbox was trained! Then it took another 2 or 3 seconds to settle and then validate. The results show an accuracy of 83%.

2018-04-20 19:33:49.833000+00:00—4——3—————-

Bayesian Linear Regression in Python: Using Machine Learning to Predict Student Grades

I want to show an example of a complete data science pipeline, we’ll walk through how to get started on a data science problem.
The first post will concentrate on defining the problem, exploratory data analysis, and setting benchmarks.
The second part will focus entirely on implementing Bayesian Linear Regression and interpreting the results, we will implement Bayesian Linear Regression in Python to build a model. After we have trained our model, we will interpret the model parameters and use the model to make predictions.
The complete code for this project is available as a Jupyter Notebook on GitHub.
2018-04-20 18:34:29.856000+00:00

Defence Against the Data Arts : Python vs R

Like a diplomatic politician I’d say that if your aim is classical statistical analysis then go with R,  if your aim is Machine Learning than go with Python as the support is better and coding easier.
But I believe that for a Data Science and ML enthusiast its good to be a jack of both the trades.
Harry Potter Python
2018-04-20 17:57:58.522000+00:00—4——5—————-

IBM outlines the 5 attributes of useful AI

A few weeks ago, a dejected CTO told me it took his team three weeks to build a machine learning model. I told him a model in just three weeks sounded great, and he agreed. So why the long face? Because 11 months later, the model was still sitting on a shelf.
That gap between great AI prototypes and AI in operation is starting to be a common theme as AI and machine learning make contact with the real world. The reason is … Actually, there are a lot of reasons, and we can look at a bunch of them, but underneath all the other reasons is the fact that data doesn’t sit still and never will.
Data changes as the world changes. Building an AI or machine learning model means building a way of looking at the world. But as the world and the data change, the models need to adapt. The CTO I met was realizing that building a great model is only the first step.
A model on its own is too brittle for the real world. It needs to live as a larger system that’s actually fluid. So how do we make AI systems that are fluid? By building them with five attributes in mind:

  1. Managed – inputs are clean
  2. Resilient – all models fall out of ‘sync’
  3. Performant – fast faster fastest
  4. Measurable – must improve the bottom line
  5. Continuous – continuous learning

2018-04-21 00:00:00

You’ve got a chip, I’ve got a chip, everybody’s got a chip – Why tech companies are racing each other to make their own custom A.I. chips

Chinese retailer and cloud infrastructure provider Alibaba is the latest company to think up its own design for processors that can run artificial intelligence software. It joins a crowded roster of companies already working on similar custom designs, including Alphabet, Facebook and Apple.
The trend could eventually threaten the traditional relationship between big buyers and big suppliers. In particular, chipmaker Nvidia, whose stock has surged as its graphics processing chips have become common for powering AI-based applications, could find its data center business impacted as these roll-your-own-chip projects mature.
2018-04-21 00:00:00

5 Reasons “Logistic Regression” should be the first thing you learn when becoming a Data Scientist

I started my way in the Data Science world a few years back. I was a Software Engineer back then and I started to learn online first. I remember that as I searched for online resources I saw only names of learning algorithms — Linear Regression, Support Vector Machine, Decision Tree, Random Forest, Neural Networks and so on. It was very hard to understand where I should start. Today I know that the most important thing to learn to become a Data Scientist is the pipeline, i.e, the process of getting and processing data, understanding the data, building the model, evaluating the results (both of the model and the data processing phase) and deployment. So as a TL;DR for this post: Learn Logistic Regression first to become familiar with the pipeline and not being overwhelmed with fancy algorithms.

So here’s my 5 reasons why today I think that we should start with Logistic Regression first to become a Data Scientist. This is only my opinion of course, for other people it might be easier to do things in a different way.

  1. The learning algorithm is just a part of the pipeline
  2. You’ll better understand Machine Learning
  3. “Logistic Regression” is (sometimes) enough
  4. It is an important tool in Statistics
  5. It is a great start to learning Neural Networks

2018-04-21 19:03:32.805000+00:00—4——2—————-

Python for Finance: Stock Portfolio Analyses

I have been investing in my own stock portfolio since 2002 and developed a financial model for my portfolio a number of years ago. For years, I would download historical prices and load the data into the financial model — while online brokers calculate realized and unrealized returns, as well as income and dividends, I like to have historical data in the model as I conduct my own analyses to evaluate positions. One view / report which I’ve never found from online brokers and services is a “Public Market Equivalent”-like analysis. In short, the Public Market Equivalent (PME) is a set of analyses used in the private equity industry to compare the performance of a private equity fund relative to an industry benchmark. Much more detail here.

Related, the vast majority of equity portfolio managers are unable to select a portfolio of stocks which outperforms the broader market, e.g., S&P 500, over the long-term (~1 in 20 actively managed domestic funds beat index funds). Even when some individual stocks outperform, the underperformance of others often outweighs the better-performing stocks, meaning overall an investor is worse off than simply investing in an index fund.

2018-04-20 13:02:22.165000+00:00


Microsoft Translator Uses AI to Break Language Barriers on Smartphones

Users of the newly-updated Translator app can now download AI-enabled translation packs, a feature that was previously available to a just a couple of smartphones.
Overall, both the online and offline flavors of Microsoft’s neural network translation technology yield faster and more fluent translations than the statistical machine translation approach of the past, said Menezes. “[We made] tremendous progress in the past few years because of machine learning and neural networks,” he said.
As an added bonus, the new language packs take up less mobile storage. The move to neural machine translation has reduced the size of Translator’s packs by 50 percent.
2018-04-18 00:00:00

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