Industry News

Industry News

Machine learning AI News covering topics of IBM Red Hat acquisition, Top 20 Lawyers beater by Legal AI, Managements two AI requirements, AI making faces, BofA lowers NVidea 12 month target attributing the change to AMDs PnL, Alternative Data – Tesla delivery Numbers, AI Art under the hammer and Microsoft’s chatbot is back.

AI & Machine Learning News. 29, October 2018

An oldie but a goldie from June 2016…

CloudQuant Thoughts… Black Mirror Season 4, Episode 5

Story of the week : IBM acquires a Red Hat…

CloudQuant Thoughts… IBM had nowhere to go, in 4th place (at least) in the cloud computing industry, they had to pair up with someone. It is interesting that 17 years ago, the valuation of Red Hat moving higher than the valuation of IBM was arguably one of the triggers of the original .com crash. Hyper-Cloud is the future.. all hail Hyper-Cloud!!

 20 Top Lawyers were beaten by legal AI. Here are their surprising responses…

In a landmark study, 20 top US corporate lawyers with decades of experience in corporate law and contract review were pitted against an AI. Their task was to spot issues in five Non-Disclosure Agreements (NDAs), which are a contractual basis for most business deals.
The study, carried out with leading legal academics and experts, saw the LawGeex AI achieve an average 94% accuracy rate, higher than the lawyers who achieved an average rate of 85%. It took the lawyers an average of 92 minutes to complete the NDA issue spotting, compared to 26 seconds for the LawGeex AI. The longest time taken by a lawyer to complete the test was 156 minutes, and the shortest time was 51 minutes. The study made waves around the world and was covered across global media.
However, surprisingly, for these defeated lawyers there was no hand-wringing. There was almost no talk of the “rise of the machines” nor fear of AI taking their jobs.
2018-10-25 13:43:32.599000+00:00 Read the full story.
CloudQuant Thoughts… AI is closing in on the Lawyers now! … click on the image to see the full infographic.

The only 2 questions management cares about when it comes to analytics

There are countless stories of analysts who spend hours and hours developing brilliant insights and presenting them to business leaders, only to be met with a lack of enthusiasm and a “So what?” The issue here is not with those business leaders. They are spot on. It is the responsibility and core function of an analytics process to provide the “So what?” by clearly articulating how their insights will answer one of two questions:

  1. How will revenue be increased?
  2. How will costs be decreased?

2018-10-28 17:24:46.563000+00:00 Read the full story.

CloudQuant Thoughts… It is so easy to lose sight of this in the maelstrom that is Data Science, the goals are very simple indeed.

Generating custom photo-realistic faces using AI – None of these images are real!

Describing an image is easy for humans, and we are able to do it from a very young age. In machine learning, this task is a discriminative classification/regression problem, i.e. predicting feature labels from input images. Recent advancements in ML/AI techniques, especially deep learning models, are beginning to excel in these tasks, sometimes reaching or exceeding human performance, as is demonstrated in scenarios like visual object recognition (e.g. from AlexNet to ResNet on ImageNet classification) and object detection/segmentation (e.g. from RCNN to YOLO on COCO dataset), etc.
However, the other way around, generating realistic images based on descriptions, is much harder, and takes years of graphic design training. In machine learning this is a generative task, which is also much more challenging than discriminative tasks, as a generative model has to produce much richer information (like a full image at some level of detail and variation) based on a smaller seed input. Despite the difficulty in creating such types of applications, generative models (with some control) can be extremely useful in many cases…
2018-10-25 17:24:19.763000+00:00 Read the full story.
CloudQuant Thoughts… Amazing technology but do they all have to be so attractive? Lets see what it can do with ugly faces!

Nvidia Upside Slimmer on AMD Earnings: BofA

Shares of semiconductor manufacturer Nvidia Corp. (NVDA) have taken a beating in the recent period, down nearly 30% over three months amid a broader sell-off in the once red-hot chip industry. Now, one team of bears on the Street warns that more bad news waits ahead for the Santa Clara, Calif.-based tech company, which is gearing up to post its most recent quarterly report on Nov. 15.
In a note to clients on Thursday, Bank of America Merrill Lynch lowered its 12-month price target on Nvidia stock from $360 to $300, attributing the more limited upside to disappointing quarterly results from rival Advanced Micro Devices Inc. (AMD), as outlined by Barron’s. Chip maker AMD has seen its shares lose nearly 40% of their value this week following earnings results in which top line numbers and weaker than expected guidance fell far from the Street’s forecasts.
2018-10-26 12:49:00-06:00 Read the full story.

CloudQuant Thoughts… We have asked you before if you can make a model to take advantage of NVDA’s strength in both the video game market and the AI/ML hardware market. Basing a price target off their nearest competitor can sometimes be a good idea, if the market as a whole is going soft but there is no indication of that here. Sometimes you just have a fierce competition between two companies in the same space and one starts to pull ahead, negatively affecting the PnL of the loser. That is only my opinion of course. I would always install an Nvidia card over an AMD card but that is my personal choice. What do you think?

Alternative data sheds light on Tesla’s mysterious delivery numbers

Tesla’s strong Q3 delivery data was good news for the company. But a report on mysterious lots of vehicles appearing across America has fanned speculation that those numbers may not be what they seem. Quandl alternative data also indicates that there may be more than meets the eye.
Quandl clients have been able to generate highly accurate projections of Tesla’s “cars-in-hand” through the use of alternative data comprising U.S. auto insurance registrations. As insurance is mandatory for U.S. drivers, new car deliveries almost perfectly correlate with the sales of new auto insurance policies.
“As Quandl data tracks action by owners of new Tesla models, we can very safely assume that it indicates a successful delivery to the end customer,” says our Chief Data Officer Abraham Thomas. “Because there is a wait list for cars, it has been assumed that Tesla’s reported delivery numbers track very closely to sales. But the widening gap between our insurance data and Tesla’s delivery numbers suggests that the company’s definition of ‘delivery’ may not necessarily reflect a customer taking ownership of a vehicle.”
“This could support the ideas posited by the NYT article, although not necessarily the worst-case scenario of sliding demand,” says Thomas.
2018-10-04 Read the full story.
CloudQuant Thoughts… This article was posted October 4th, Tesla reported “Blowout earnings” on 24 October 2018…

  • Tesla’s shares jumped by more than 12 percent after the company reported a surprise profit for the third quarter.
  • Tesla gave investors hope that its production rates will improve, saying that the number of labor hours to build the Model 3 fell.
  • This is CEO Elon Musk’s last earnings report as chairman for at least three years.


How three French students used borrowed code to put the first AI portrait in Christie’s

On Thursday, October 25th, Christie’s will conduct a very unusual sale. As part of a three-day Prints & Multiples event, it’s auctioning off the Portrait of Edmond Belamy, a canvas in a gold frame that shows the smudged figure of what looks like an 18th century gentleman. It’s expected to fetch a modest price, somewhere between $7,000 and $10,000, but the artwork’s distinguishing feature is that it was “created by an artificial intelligence,” says Christie’s. “And when it goes under the hammer, [it] will signal the arrival of AI art on the world auction stage.”
But for members of the burgeoning AI art community, there’s another attribute that sets the Portrait of Edmond Belamy apart: it’s a knock-off. The print was created by Obvious, a trio of 25-year-old French students whose goal is to “explain and democratize” AI through art. Over the past year, they’ve made a series of portraits depicting members of the fictional Belamy family, amplifying their work through attention-grabbing press releases. But insiders say the code used to generate these prints is mostly the work of another artist and programmer: 19-year-old Robbie Barrat, a recent high school graduate who shared his algorithms online via an open-source license. The members of Obvious don’t deny that they borrowed substantially from Barrat’s code, but until recently, they didn’t publicize that fact either. This has created unease for some members of the AI art community, which is open and collaborative and taking its first steps into mainstream attention. Seeing an AI portrait on sale at Christie’s is a milestone that elevates the entire community, but has this event been hijacked by outsiders?
2018-10-23 00:00:00 Read the full story.
CloudQuant Thoughts… and it sold for $432,500, so if you don’t mind.. I am of to clone that code…

Microsoft’s chatbot push packed with emotion

Microsoft’s most famous employee in China is a 16-year-old girl who can chat with you, sing a song, tell your kids bedtime stories and give you a wake up call. What’s more, she’s never needs a day off or even a break. If cloud computing is the fuel that has helped propel Microsoft towards the magic $US1 trillion valuation mark, then it is artificial intelligence that represents its next big bet. During Chanticleer’s recent tour of the Microsoft campus at Redmond, just outside of Seattle, the lengths the company is going to incorporate AI into its business (or “infuse” it, as the company’s executives like to say) was made clear. But one of the AI projects I struggled most to get my head around originated in China, where the company has deployed a chat bot called Xiaoice.
While Xiaoice can help you with simple tasks – such as that wake-up call – she has not been designed as virtual assistant, but rather a sort of emotional support system that can engage with users through a proper conversation. A user can exchange text-based messages with Xiaoice, asking her questions, rant about life problems, or seek support to get through a break-up. Xiaoice will reply with appropriately soothing responses, tell jokes and generally monitor the user’s life. If the user has been ill, for example, Xiaoice will ask about the user’s health he next time they log on. If they have a big exam coming up, Xiaoice might jokingly chastise the user for procrastinating. She will also let her emotions show. If you ask her age too often, apparently she becomes a little irritated.
2018-10-28 00:00:00 Read the full story.

CloudQuant Thoughts… You can’t knock them for trying, after the last debacle anyone would have excused them for completely giving up on the idea, but no, ZO is here and she (she again… hmmm) has #friendgoals, whatever those are.

Below the fold…

Microsoft to U.S. military: We’ve got your six

Microsoft intends to continue to work with the U.S. military, president Brad Smith said today. New technology like artificial intelligence and autonomous weaponry are raising ethical and legal challenges, Smith acknowledged, and he said no tech company has been more active than Microsoft in addressing legal and public policy issues.
Smith spoke via a blog post today after a meeting with Microsoft employees Thursday, and follows a push by Smith to demand the federal government regulate the use of facial recognition software. “All of us who live in this country depend on its strong defense. The people who serve in our military work for an institution with a vital role and critical history,” he said. “We want the people of this country and especially the people who serve this country to know that we at Microsoft have their backs. They will have access to the best technology that we create.”
2018-10-26 00:00:00 Read the full story.


Why we need more than just data to create ethical driverless cars

What do we want driverless cars to do in unavoidable fatal crashes? Today researchers published a paper The Moral Machine experiment to address this question.
To create data for the study, almost 40 million people from 233 countries used a website to record decisions about who to save and who to let die in hypothetical driverless car scenarios. It’s a version of the classic so-called “trolley dilemma” – where you have to preference people to prioritise in an emergency. Some of the key findings are intuitive: participants prefer to save people over animals, the young over the old, and more rather than fewer. Other preferences are more troubling: women over men, executives over the homeless, the fit over the obese.
2018-10-29 14:15:53+11:00 Read the full story.


Walmart’s test store for new technology, Sam’s Club Now, opens next week in Dallas

Walmart’s warehouse club, Sam’s Club is preparing to open the doors at a new Dallas area store that will serve as a testbed for the latest in retail technology. Specifically, the retailer will test out new concepts like mobile checkout, an Amazon Go-like camera system for inventory management, electronic shelf labels, wayfinding technology for in-store navigation, augmented reality, and artificial intelligence-infused shopping, among other things.
2018-10-28 00:00:00 Read the full story.
2018-10-29 00:00:00 Read the full story.

Self Learning AI-Agents Part II: Deep Q-Learning – Towards Data Science

A mathematical Guide through Deep-Q Learning. In this second part of the multi-part series on Deep Reinforcement Learning I will present you an effective approach to how AI agents can learn to behave in environments with discrete action spaces.
Self Learning AI-Agents Series — Table of Contents

  • Part I: Markov Decision Processes
  • Part II: Deep Q-Learning (This article)
  • Part III: Deep (Double) Q-Learning
  • Part IV: Policy Gradients for Continues Action Spaces
  • Part V: Dueling Networks
  • Part VI: Asynchronous Actor-Critic Agents

2018-10-28 15:05:16.206000+00:00 Read the full story.

Mastering ‘Big Data’ is the Key to Financial Success

Running a business is no easy task, particularly in uncertain times with economic and digital disruption causing ongoing issues for company leaders. In the capital markets sector, these challenges are perhaps more acute, with investor apprehension starving organisations of extra funding, at a time when digital innovation is critical. It is in this scenario that the case for better management of company data becomes clear. The sector is increasingly harnessing the power of information to enable better internal audits, improve transaction reporting, and prevent fraud and crime. For Chief Financial Officers (CFOs) these abilities are seen as a major asset, with 77 per cent saying they see a future where their ability to transform financial data into intelligence will be a necessity to drive growth.
As well as this, in a recent analytics survey, 34 per cent of senior decision makers said they had issues around the practical management of data challenges rather than a lack of skills or technology within the workforce. The research also found that 74 per cent of those questioned admitted to limiting themselves to the data they have immediately available, instead of looking at how analytics can enable sharper insights and predictions about future trends for the business.
2018-10-26 14:52:37+00:00 Read the full story.


How to Interpret a Random Forest Model (Machine Learning with Python)

Machine Learning is a fast evolving field – but a few things would remain as they were years ago. One such thing is ability to interpret and explain your machine learning models. If you build a model and can not explain it to your business users – it is very unlikely that it will see the light of the day. Can you imagine integrating a model into your product without understanding how it works? Or which features are impacting your final result? In addition to backing from stakeholders, we as data scientists benefit from interpreting our work and improving upon it. It’s a win-win situation all around!
The first article of this machine learning course saw an incredible response from our community. I’m delighted to share part 2 of this series, which primarily deals with how you can interpret a random forest model. We will understand the theory and also implement it in Python to solidify our grasp on this critical concept. As always, I encourage you to replicate the code on your own machine while you go through the article. Experiment with the code and see how different your results are from what I have covered in this article. This will help you understand the different facets of both the random forest algorithm and the importance of interpretability.
2018-10-29 08:32:25+05:30 Read the full story.


Mr. Robot goes to Washington: How AI will change democracy

By gathering together and synthesizing large amounts of the available data–giving equal consideration to everyone’s interests, preferences, and values–we could create the sharpest and fullest possible portrait of the common good. Under this model, policy could be based on an incomparably rich and accurate picture of our lives: what we do, what we need, what we think, what we say, how we feel. The data would be fresh and updated in real time rather than in a four- or five-year cycle. It would, in theory, ensure a greater measure of political equality–as it would be drawn from everyone equally, not just those who tend to get involved in the political process. And data, the argument runs, doesn’t lie: it shows us as we are, not as we think we are.
Machine-learning systems are increasingly able to infer our views from what we do and say, and the technology already exists to analyze public opinion by processing mass sentiment on social media. Digital systems can also predict our individual views with increasing accuracy. Facebook’s algorithm, for instance, needs only 10 “likes” before it can predict your opinions better than your colleagues, 150 before it can beat your family members, and 300 before it can predict your opinion better than your spouse. And that’s on the basis of a tiny amount of data compared to the amount that will be available in the future.The logical next question is this: what role will artificial intelligence come to play in governing human affairs?
2018-10-26 14:00:39 Read the full story.

AI Weekly: Building AI-first companies requires more than hiring data scientists

This week, VentureBeat held its annual VB Summit 2018, an intimate gathering of business executives to discuss the role AI can play in transforming their business.
Throughout boardroom sessions and onstage interviews, one issue companies repeatedly raised is difficulty finding data scientists. No surprise there. This shortage has been well-documented for some time now, as competition for machine learning experts and talent to develop neural nets has resulted in salaries comparable to those of professional athletes. But there’s more to it than that. Another idea frequently repeated at VB Summit was that transforming to a data-driven business requires change from the wider organization, not just the machine learning experts themselves.
Though some may believe trying to explain ML to business executives is a waste of time, CI&T chief digital evangelist Lucas Persona believes the biggest breakthroughs belong to companies that enable data science teams to work alongside other employees. Working shoulder-to-shoulder with the rest of the company means a data science team should not be treated like the wizard behind the curtain or philosophers in an ivory tower. “ML has become in many organizations more and more … siloed. We are the ML team, the DS team, and every time somebody comes to talk to us, we will say these crazy words [and] they will feel bad about themselves and leave, so it’s kind of off-putting,” he said.
2018-10-26 00:00:00 Read the full story.

Google updates Firebase … ML Kit Face Contours

…Google is expanding ML Kit’s Face Detection API with the beta launch of face contours, letting developers detect over 100 detailed points in and around a user’s face. Face contours lets apps handle tasks like overlaying masks and accessories on facial features (ears, eyes, nose, mouth, and so on) or adding beautification elements, like skin smoothing and coloration.
Google launched ML Kit for Android and iOS developers at its I/O developers conference in May. ML Kit is supposed to make machine learning easy for all app developers, whether you’re using its APIs or bringing your own custom TensorFlow Lite models and serving them through Firebase.
2018-10-29 00:00:00 Read the full story.


Weekly Selection — Oct 26, 2018 – Towards Data Science

  • The 5 Basic Statistics Concepts Data Scientists Need to Know
  • AI Insights for Human Intelligence
  • Transfer learning from pre-trained models
  • Lessons Learned from Applying Deep Learning for NLP Without Big Data
  • Building an ETL Pipeline in Python
  • Text Predictor – Generating Rap Lyrics with Recurrent Neural Networks (LSTMs)
  • NeuralFunk – Combining Deep Learning with Sound Design
  • SQL at Scale with Apache Spark SQL and DataFrames – Concepts, Architecture and Examples
  • How I Exploited My Own Social Media Data

2018-10-26 16:33:18.341000+00:00 Read the full story.


Pandas Transform — More Than Meets the Eye – Towards Data Science

Lately I’ve been working with Pandas. While working on a project I encountered a nifty function I hadn’t known about, and after asking around it seems I’m not the only one missing out, so let’s remedy that.
2018-10-28 13:31:06.911000+00:00 Read the full story.


A.I. Is Helping Scientists Predict When and Where the Next Big Earthquake Will Be

SAN FRANCISCO — Countless dollars and entire scientific careers have been dedicated to predicting where and when the next big earthquake will strike. But unlike weather forecasting, which has significantly improved with the use of better satellites and more powerful mathematical models, earthquake prediction has been marred by repeated failure.
Some of the world’s most destructive earthquakes — China in 2008, Haiti in 2010 and Japan in 2011, amo…
2018-10-26 00:00:00 Read the full story.

The Scent of an AI

“A fresh, fruity floral with an innovative twist in the top note.” That’s how master perfumer Dave Apel describes the first perfume created by artificial intelligence, which a Brazilian beauty company plans to start selling in 2019.
Now, machines can’t smell, at least not like humans can. But as a result of work that fragrance manufacturer Symrise AG did with IBM Research, you can now add “creating great perfumes” to the list of things that AI-powered robots can do as well as humans. Symrise and IBM collaborated to develop Philyra, an AI-based system that’s similar in some respects to Chef Watson, the IBM program that has turned some heads for its capability to create tasty recipes by utilizing interesting combinations of foods and flavors. The companies took that work as a starting point, and tweaked it to handle fragrances and formulas.
2018-10-23 00:00:00 Read the full story.


Sisense Hunch Turns IoT Devices Into Supercomputers

…Sisense Hunch ‘learns’ massive datasets and can produce microsecond analytical responses to queries that are 99 percent accurate or better, with a tiny fraction of the cost and storage footprint. Sisense Hunch represents a new class of big data analytics, data cognition engines, which can be applied to a number of revolutionary applications that were impossible due to latency and cost. Sisense Hunch puts the power of tens of billions of rows of data into a small, portable, cost-effective, and secure Internet of Things (IoT) package – effectively turning sensors, phones, and wearables into supercomputers.
“Sisense Hunch achieves the impossible, taking large datasets that require massive amounts of computing power and storage, and making them digestible with an edge IoT device. Once a Sisense Hunch neural network learns data, it doesn’t need any ongoing access to the complete underlying data set, allowing it to achieve lightning-fast, analytical query responses, with minimal costs, while maintaining complete data privacy. That’s what makes it possible to move ‘supercomputing’ to the ‘edge,’ turning IoT devices from data collectors into smart data analyzers.” – Amir Orad, CEO, Sisense.
Sisense Hunch is currently in testing with multiple Sisense clients, and has been used to streamline efficiencies around quality control at a production facility of a publicly listed electronics manufacturer. Before Sisense Hunch, obtaining big data insights through high performance databases was slow and expensive, so they were off-limits to most business users and processes. With Sisense Hunch, massive stores of data are transformed into mere megabytes allowing for microsecond response times and the ability to be placed anywhere, even inside a tiny IoT device.
2018-10-26 00:05:40-07:00 Read the full story.


IBM explores the intersection of AI, ethics–and Pac-Man

As IBM’s researchers thought about the challenge of making software follow ethical guidelines, they decided to conduct an experiment on a basic level as a project for some summer interns. What if you tried to get AI to play Pac-Man without eating ghosts—not by declaring that to be the explicit goal, but by feeding it data from games played by humans who played with that strategy? That training would be part of a special sauce that also included the software’s unconstrained, self-taught game-play techniques, giving it a playing style influenced by both human and purely synthetic intelligence. Stepping through this exercise, IBM’s researchers figured, might provide insights that would prove useful in weightier applications of AI.
IBM chose Pac-Man as its tapestry for this experiment partly out of expedience. The University of California, Berkeley has created code for an instrumented version of Pac-Man designed for AI studies; the company was able to adapt this existing framework for its purposes. (Teaching AI to play Ms. Pac-Man is a separate science unto itself, and a more imposing challenge, given the game’s greater complexity.) The researchers built a piece of software that could balance the AI’s ratio of self-devised, aggressive game play to human-influenced ghost avoidance, and tried different settings to see how they affected its overall approach to the game. By doing so, they found a tipping point—the setting at which Pac-Man went from seriously chowing down on ghosts to largely avoiding them.
2018-10-25 11:35:58 Read the full story.
2018-10-29 00:00:00 Read the full story.

Data Centers: An Ideal Use Case for Industrial AI

…there are many established metrics for measuring and operating data centers, including power efficiency, availability, and space utilization. However, with the shifts in the data center market, are we now even focused on the right metrics? For the existing metrics, are we capturing the right data points? Are there hidden metrics and patterns waiting to be exploited to maximize the utility of these facilities?
To address these points, let’s first consider the data center facilities themselves. Simply, the scale, complexity and required optimization of these facilities require “management by AI” as they increasingly cannot be planned and managed with traditional rules and heuristics.
2018-10-23 00:00:00 Read the full story.


Staying Viable: Highlights From the T3 Enterprise Conference

Machine learning, artificial intelligence and cybersecurity were big themes at this year’s technology-focused T3 Enterprise Conference in Las Vegas. From keynotes describing what the future of financial services could look like to the demonstration of products and announcements of new partnerships, the conference took a broad swing at the way technology is shaping the advisors’ practices. TD Ameritrade Institutional’s Dani Fava, the firm’s director of product strategy, delivered a keynote covering technology her firm is keeping a close eye on. One of her suggestions for advisors: find and familiarize yourself with a compliant text messaging solution.
Advisors interested in communicating with the next generation of wealth, millennials, need to learn how to communicate with potential clients who prefer to send messages instead of making phone calls, said Fava. She cited Domino’s Pizza as an innovative example of a multichannel communicator: taking pizza orders via phone calls, texted emojis, through its app and on its website. Furthermore, advisors who’ve been dismissive of AI and machine learning should rethink their position, cautioned Fava. “Every single industry is thinking about this, including financial services. If the companies you’re working for, or with, are not thinking about AI, they’re not going to be viable in the future,” she said.
2018-10-25 16:02:09-04:00 Read the full story.


New Cloudera Plots a Course Toward a Unified Future

The merger of Hortonworks and Cloudera will eliminate competition in the market for big data platforms and create a clear leader in the space. Once the transaction is complete, the new Cloudera will embark upon the challenging task of merging the two companies’ offerings into a unified data platform that maintains links to Hadoop’s legacy past while providing an upgrade path to its “edge to cloud” architecture of the future.
2018-10-24 00:00:00 Read the full story.


Hot DataRobot Raises a Bundle

The bucks keep rolling in from technology investors pouring cash into machine learning and data science startups. The latest beneficiary is DataRobot, the machine learning automation vendor. Formed in 2012, the company has so far raised $225 million, a total that includes a whopping $100 million Series D funding round announced this week. The round was led by Meritech and Sapphire Ventures, with participation from 17 previous investors that include DFJ Growth, IA Ventures, Intel Capital and New Enterprise Associates.
Boston-based DataRobot said Thursday (Oct. 25) it would use the new funding to expand its global operations and its product portfolio. The company’s proprietary data science and machine learning automation platform is designed to automate the building of machine learning models. It is gaining steady traction among business analysts and other non-data scientists.
2018-10-25 00:00:00 Read the full story.


Why Data Scientists Love the Law of Supply and Demand

It should come as no surprise that demand for data scientists keeps going up. However, supply of data scientists has not kept pace, unfortunately (or fortunately, depending on your point of view). Thanks to the law of supply and demand, companies are being asked to pay annual salaries well into the six figures – and sometimes even seven or eight figures – to attract and retain top AI talent.
In February, Element released an analysis of LinkedIn profiles that concluded there are only about 22,000 Ph.D.-carrying data science researchers and engineers worldwide who have the technical skills to deploy deep learning methodologies in a commercial setting. What’s more, only about 3,000 of those data scientists are currently looking for a job – although it’s common for companies to poach top data talent from competitors.
2018-10-25 00:00:00 Read the full story.


Automation Gunning for Tech Jobs (And That’s a Good Thing!)

As artificial intelligence (A.I.), machine learning, and automation increasingly enter the strategic planning of many companies, many tech pros are wondering if their jobs are at risk. Via a new survey from Nintex, we now have a better idea of how automation may change or eliminate tech-related jobs.
Of the ‘decision makers’ on automation (CEOs, project managers, and other executive-level staff), 71 percent say automation will affect up to one-fifth of the positions at their companies. It’s important to note that this isn’t specific to the technology world; the study spans several industries.
2018-10-26 00:00:00 Read the full story.


VantagePoint ai incorporates Artificial Intelligence into its trading software – (Video 8:10)

VantagePoint ai President Lane Mendelsohn tells Proactive Investors the software company is using Artificial Intelligence to give traders advanced notice of trend changes, allowing them to enter and exit positions at the optimal time.
Mendelsohn says the company is using AI to mine data using machine learning to forecast stock prices with a “very high degree of accuracy.”
2018-10-24 00:00:00 Read the full story.

ING Brings AI to Bond Trading

ING has created an artificial intelligence tool with Dutch asset manager PGGM to help investors make quicker decisions on which bonds to buy and sell.
The Dutch bank originally developed Katana to help its traders provide liquidity and respond more quickly to requests for quotes from clients. Katana provides traders with a visualization of relevant historic and real-time data and the tools’s algorithms provide forward looking predictions of the price that will win an RFQ within a certain confidence range.
2018-10-26 12:22:37+00:00 Read the full story.


Data-as-a-Service: Helping Companies Get More Value From Their Data, Faster

Roger Magoulas introduced us to the term Big Data back in 2005. Never did we imagine that data was going to increase in volume so fast that the term itself would become almost irrelevant and big data would become just ‘data’. Big Data also brought with itself concepts like the 4v’s of Big Data in addition to the the definition of the Data Lake, several sources of data available in the same repository. However, this concept would rapidly become an Enterprise Data Swamp for many. The reason, by nature, Data Lakes accept any type of data, but without cataloging, curation, governance, security, and proper oversight, it will turn into costly data chaos adding to the $3 trillion that bad data costs us each year in the US.
2018-10-26 00:35:59-07:00 Read the full story.


dotData Releases Updates to the First and Only End-to-End Data Science Automation Platform

According to a new press release, “dotData, the first and only company focused on delivering end-to-end data science automation and operationalization for the enterprise, today announced the availability of Version 1.2 of its dotData Platform. The new version adds significant enhancements to the platform, enabling users to have even deeper insights, more transparency, and greater business impacts in the development and operationalization of their data science projects. The AI-powered dotData Platform completely automates the entire data science process, from data collection through production-ready models. As a result, the entire data science process is accelerated from months to days, enabling companies to rapidly scale their AI/ML initiatives to drive transformative business changes.”
2018-10-26 00:15:48-07:00 Read the full story.


Tableau Improves Analytics with Natural Language Product “Ask Data”

At its annual conference in New Orleans, Tableau Software announced its product roadmap featuring “Ask Data,” which leverages natural language processing to enable people to ask questions in an intuitive, conversational manner that makes it easier for far more people to engage with data and produce analytical insights.
Tableau’s new Ask Data allows people to ask questions in plain language and instantly get a response. Tableau will return an interactive visualization with no need to learn data dimensions, measures, or any data structure. According to Tableau, Ask Data uses sophisticated algorithms that are driven by an understanding of the person’s intent, not keywords, which helps Tableau understand a person’s question, anticipate needs, and allow for smart visualization selection.
2018-10-23 00:00:00 Read the full story.


Cybersecurity risk curbing enterprise adoption of AI

…firms highlighted an array of risks surrounding AI technology that are causing concern, including algorithms making the wrong decisions, the legal ramifications of their decisions, the consequences of system failure, and regulatory risk.
Topping firms’ concerns are the cyber risks that AI brings to the table. 23 percent of respondents ranked “cybersecurity vulnerabilities” as their no. 1 overall risk concern, with 32% reporting suffering an AI-related cybersecurity breach in the past two years.
2018-10-23 00:00:00 Read the full story.

China’s Baidu challenges Google with A.I. that translates languages in real-time

Baidu unveiled an artificial intelligence-powered tool that can translate languages in real time on Wednesday.
The simultaneous translation feature has been trained on two million pairs of English and Chinese sentences.
The Chinese tech giant is posing a challenge to Google which last year unveiled its real-time translation tool.
2018-10-24 00:00:00 Read the full story.


Oracle Adds AI, Machine Learning Features to Its Cloud Services

Oracle revealed some new artificial intelligence updates to Oracle Enterprise Resource Planning Cloud and Enterprise Performance Management Cloud. Like past improvements, these all extract data-driven insights from enterprise data stores, helping companies capitalize on new market opportunities and increase profitability. However, according to Oracle Senior Vice President of Applications Product Development Steve Miranda, “The main news [Oracle is] announcing is happening in three main categories. First … machine learning is going across the board. You’ll see more bots in [Oracle’s] interfaces, for one thing. This will allow you to become more conversational and interactive with the apps.”
2018-10-25 00:00:00 Read the full story.


Retailers lagging in pick-up of AI, Microsoft finds

Britain’s retailers are integrating artificial intelligence into their operations at a much slower pace than other major industries, Microsoft will reveal this week, casting a light on the crisis on the high street. A research report by Microsoft, which will be released on Wednesday, will show that over half (56pc) of UK retail companies are still not using AI into their operations, when compared to 44pc of financial services companies and 40pc of manufacturers. Of the leaders in the retail sector, around 61pc are currently using AI technology.
“Clearly, for the sector to thrive in the future, the speed of change must increase,” Microsoft will say. “Yet, in fact, many experts predict adoption will remain slow, particularly in the big box retail sector due to the challenges of scaling their digital offer quickly and cost-effectively enough to compete with native online retailers. “What is clear is that change cannot be ignored. AI will impact retailers of all shapes and sizes.”
2018-10-28 00:00:00 Read the full story.


Humanization Is Key to Making AI Projects Successful

Artificial intelligence is routinely touted at tech conferences and elsewhere as the “Next Big Thing” that is going to transform the customer experience and the ability of companies to better sell and market their wares. But there were also skeptical and cautionary notes sounded here, even from vendors, at the Connected Enterprise conference (running Oct. 22-25) sponsored by Constellation Research.
“There are a lot of misconceptions about what AI can do in the enterprise. I would focus on really picking a specific problem,” said Inhi Cho Suh, general manager of Watson Customer Engagement at IBM. For customers of IBM’s Watson AI supercomputer services, Suh said it’s important to focus on precise algorithms for small sets of data. “The language of business is incredibly unique,” said Suh. “Ask the marketing team or the supply team for the definition of ‘customer’ and ‘order,’ and you might get different answers.”
2018-10-25 00:00:00 Read the full story.


Every organisation now wants an analytics platform – Here is why! – Big Data Made Simple

With the nascent stage of the data revolution past us, organisations are entering a new level of proficiency in handling data expertly. Gone are the days when organisations could handle data and extract insights out of it, without the presence of an analytics platform.
The importance of enterprise analytics platforms has grown over time, to such an extent that they are considered imperative for storing and analysing data today.
2018-10-26 22:46:57+00:00 Read the full story.


Effectively Utilizing Publicly Accessible Social Media Data While Staying GDPR Compliant

As society moves towards an even deeper reliance on social media, the distinction between one’s private life, social media life and professional life is increasingly skewered and distorted. More and more, young people are choosing not to keep private profiles searching instead to be reachable, to become influencers or to be the next trending tweet, Instagram photo, or YouTube cover.
At the same time, technology and data science are moving at an exponential pace providing for artificial intelligence and machine learning capable of predicting one’s interests, preferred products, or even electoral decisions, on the basis of benign information found on public social media profiles. Indeed, major corporations such as IBM are pouring billions of dollars on data analysis with budgets totaling over $24 billion. In 2014, AXA distributed Withings Pulse health wristbands to policyholders to collect and analyze their health data. The incentive for participants was to benefit from a €100 discount from their insurance policy whenever they walked more than 10,000 steps per day.
2018-10-26 14:39:22 Read the full story.


How Big Data Is Helping To Lower Medical Liability Risks

Practice economics are impacted by medical liability risks. Patient quality and efficiency is key to the healthcare industry’s success, buta lack of proper staffing has led to a fast-paced environment where medical liability remains a concern. Data collection is being utilized as a means to help lower these risks, increase profits, predict outcomes, offer better outcomes and even reduce waste. Big data can help make hospitals and care centers more efficient than before. There are already some groups that are using these new solutions to cut back on medical malpractice claims and improve the outcome for patients.
2018-10-25 18:17:39+00:00 Read the full story.


Cisco chief Chuck Robbins … AI advances

… In spite of the vagaries of the political landscape, Mr Robbins is piloting Cisco successfully through a period of change at the company, where it has sought to focus on dominating the artificial intelligence era in networking, as opposed to a strategy of diversification, which had been underway in the latter years of long-standing predecessor John Chambers’ reign. It has stepped into the AI era, not via software bots, but in the foundations of the internet, developing so-called intent-based networking, which uses machine learning to make networks capable of autonomously taking actions in anticipation of demands, threats, and likely humans and machines.
Its most recent results showed revenue and profit numbers moving in the right direction and Mr Robbins said he feels confident that he has managed to stamp his own identity on the company, having been in the hot seat for three years. “There were several things that we set out to do a few years ago and we’re beginning to see the results now come to fruition,” Mr Robbins said.
2018-10-26 00:00:00 Read the full story.


Decoded Entity Embeddings of Categorical Variables in Neural Networks

Entity Embeddings of Categorical Variables in Neural Networks
Categorical variables are known to hide and mask lots of interesting information in a data set and many times they might even be the most important variables in a model. A good data scientist should be capable of handling such variables effectively and efficiently. If you are a smart data scientist, you’d hunt down the categorical variables in the data set, and dig out as much informa…
2018-10-28 22:15:11.867000+00:00 Read the full story.

The 3 most over-hyped design trends of today

It’s easy for a trend to turn into a cliché–popular ideas have a way of becoming ridiculous when taken too far. At the Fast Company Innovation Festival, Co.Design editor Suzanne LaBarre sat down with several honorees of the 2018 Innovation by Design awards to discuss the wild future of design–and to find out which trends are vastly over-hyped right now.


2018-10-25 08:00:57 Read the full story.

Create a CI/CD pipeline for your IoT Edge solution with Azure DevOps

Azure IoT Edge is a fully managed service that delivers cloud intelligence locally by deploying and running artificial intelligence (AI), Azure services, and custom logic directly on cross-platform IoT devices. You can build your Azure IoT Edge solution using C#, Python, Node.js, C and Java and containerize your code with Windows or Linux containers. Or you can bring Azure services such as Functions, Stream Analytics, and Machine Learning to the edge.
Modern software moves fast and demands more from developers than ever. Tools and concepts around CI/CD help developers deliver value faster and more transparently. For Azure IoT Edge, we provide two tools to create CI/CD pipeline. In this post, we would introduce Azure IoT Edge for DevOps.
2018-10-29 00:00:00 Read the full story.


Artificial Intelligence Will Save The Mental Health Care Crisis

The idea of using Artificial intelligence (AI) to help people deal with their mental health battles isn’t a brand new idea. The first chatbot therapist, ELIZA, was created in 1964. However, big companies that are working on ELIZA’s modern offspring aren’t executing very well. Research by Stanford psychiatrist Adam Miner reports that companies like Apple, Microsoft, and Amazon aren’t currently training their personal assistants how to effectively respond to serious mental health queries.
With big companies moving slow, startups are prepared to fill the need and serve the people who are ready for solutions that eliminate the common barriers and deliver the best that technology has to offer. Alongside two friends, a psychiatrist (Jose Hamilton) and a designer (Diego Dotta), I created Youper to be the first-responder for emotional health problems for people all over the world and to ensure that no one has to wait years to address their issues. Just like Google became our second intellectual brain, Youper is becoming our second emotional brain: a personal assistant that users love and feel compelled to share with people they care about. Check what people are talking about it on Twitter.
Youper uses artificial intelligence (AI) to personalize techniques drawn from different psychological therapies, including Cognitive Behavioral Therapy (CBT), Acceptance and Commitment Therapy (ACT) and Meditation, to fit the users’ needs and style. I believe that advances in AI and Natural Language Processing present a unique opportunity to reduce the barriers to access emotional wellbeing.
2018-10-25 20:24:44.985000+00:00 Read the full story.

British ad startup LoopMe raises $17m as investors pile into AI

A British start-up which helps brands to tailor mobile adverts to individual viewers using artificial intelligence has raised $17m (£13m), in the latest show of growing investor interest in the space. LoopMe operates a video software platform that inserts video adverts into mobile websites or apps based on when a consumer is most likely to be interested in or wants to buy something. The technology, which uses artificial intelligence to determine consumer interest, is already used to market brands including Norwegian Air, Audi and Ben & Jerry’s.
2018-10-28 00:00:00 Read the full story.


Tech that ‘threatens democracy’ is being funded by UK taxpayers

New technology which MPs say “threatens democracy” and which has been described as a dangerous “propaganda weapon” is being funded by UK taxpayers. “Deepfakes” is a term used to describe artificial intelligence that mimics facial expressions. It can be used to build sophisticated propaganda videos by making anyone say things they haven’t said with uncanny realism.
A UK company, Synthesia, is a pioneer of the technology, which could, for example, be used to produce convincing video of Donald Trump making abusive remarks about Muslims or Vladimir Putin declaring war on Britain.
2018-10-27 00:00:00 Read the full story.


Can your Ad company really meet your needs?

“On the one hand you have companies with a massive technology core and some services wrapped around them. That’s the one you really want. On the other side, you have companies with the tiniest core of technology but with lots of services wrapped around that tiny core.”
He said in a market like Australia, which is relatively small, brands still have a lot of choices to make. “There are two dozen DSPs operating in Sydney alone,” he said. The problem he suggested is that many of the service providers lack the technological clout needed to really meet the needs of brands. “They have limited ability to actually demonstrate real technology and real artificial intelligence.”
2018-10-29 14:45:09+11:00 Read the full story.


Behind a Paywall or Registration Wall…

Do People Trust Algorithms More Than Companies Realize?

his assumption, and a series of studies found that people do not dislike algorithms as much as prior received wisdom might have us believe.
Many companies have jumped on the “big data” bandwagon. They’re hiring data scientists, mining employee and customer data for insights, and creating algorithms to optimize their recommendations. Yet, these same companies often assume that customers are wary of their algorithms — and they go to great lengths to hide or humanize them.
For example, Stitch Fix, the online shopping subscription service that combines human and algorithmic judgm…
2018-10-26 15:00:38+00:00 Read the full story.

Self-service Big Data Analytics on Microsoft Azure

In this presentation Microsoft will join Cloudera to introduce a new Platform-as-a-Service (PaaS) offering that helps data engineers use on-demand cloud infrastructure to speed the creation and operation of data pipelines that power sophisticated, data-driven applications – without onerous administration.
2018-10-26 00:00:00 Read the full story.


The Future of Data Warehousing

Data warehousing is alive, but perhaps not alive and well. Legacy data warehouses must modernize to fit gracefully into modern analytics ecosystems. They play an important role in data management as an archive of enterprise history and a source of carefully curated and highly integrated data for a broad scope of line-of-business information needs. To continue filling that role well, they must evolve both architecturally and technologically. Yet i…
2018-10-26 00:00:00 Read the full story.


Harnessing the Cloud to Deliver Big Data Insights

Accelerate insights from analytics with managed cloud services. Enterprises require fast, cost-efficient solutions to the familiar challenges of engaging customers, reducing risk, and improving operational excellence to stay competitive. The cloud is playing a key role in accelerating time to benefit from new insights.
Managed cloud services that automate provisioning, operation, and patching will be critical for enterprises to leverage the full…
2018-10-26 00:00:00 Read the full story.


To Get Your Team to Use Data, Demystify It

Billy Beane, front office executive for the Oakland Athletics and the subject of Michael Lewis’s book Moneyball, transformed baseball using data. He didn’t do it using complicated algorithms. He did it by asking an important question: What kinds of players in the Major League draft typically go on to have the most successful professional careers? He used years of data to answer that question, and then drafted players with those attributes (e.g. t…
2018-10-26 13:00:28+00:00 Read the full story.

Cloudera Enterprise: The Modern Platform for Machine Learning and Analytics Optimized for the Cloud

Many of the world’s largest companies rely on Cloudera’s multi-function, multi-environment platform to provide the foundation for their critical business value drivers—growing their business, connecting products and services, and protecting their business. Find out what makes Cloudera Enterprise different from other data platforms.
2018-10-26 00:00:00 Read the full story.

How a Pharma Company Applied Machine Learning to Patient Data

By some estimates, health data volumes are increasing by 48% annually, and the last decade has seen a boom in the collection and aggregation of this information. Among these data, electronic health records (EHRs) offer one of the biggest opportunities to produce novel insights and disrupt the current understanding of patient care. But analyzing the EHR data requires tools that can process vast amounts of data in short order. Enter artificial inte…
2018-10-25 16:00:34+00:00 Read the full story.

The Role of a Manager Has to Change in 5 Key Ways

Management has long been associated with the five basic functions: planning, organizing, staffing, directing, and controlling. These default dimensions are sufficient when pursuing a fixed target in a stable landscape. But take away the stability of the landscape, and one needs to start thinking about the fluidity of the target. This is what’s happening today, and managers must move away from the friendly confines of these five tasks. To help org…
2018-10-26 12:05:21+00:00 Read the full story.

How T-Mobile Brought Collaboration to Customer Service

The results are impressive: In three years, T-Mobile has dramatically reduced its customer churn rate, cost to serve, and employee attrition and absenteeism. Its Net Promoter Score is way up too. Other companies might likewise benefit from similar efforts to rethink standard industry practices.
There are no rows of service agents robotically responding to random calls as quickly as possible. Instead, T-Mobile relies on colocated, collaborative t…
2018-11-01 04:00:00+00:00 Read the full story.

Apple News’s Radical Approach: Humans Over Machines

Google, Facebook and Twitter have long insisted they are tech entities and not arbiters of the truth. The chief executive of Facebook, Mark Zuckerberg, and others have bet heavily on artificial intelligence to help them sort through false news and fact-based information. Yet Apple has unabashedly gone the other direction with its human-led approach, showing that a more media-like sensibility may be able to coexist within a technology company.
Apple’s strategy is risky. While the company has long used people to curate its App Store, the news is far more contentious. The famously secretive company ha…
2018-10-25 00:00:00 Read the full story.

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