Industry News

Industry News

Machine learning AI News covering Kai-Fu Lee’s new book and the US v China AI face-off – the NEW industrial revolution. Orchestrating Data Analytics – How Investment Funds are navigating this brave new world. XAI Explainable AI, what is inside the black box – being able to explain the why is very important for some disciplines including trading. Why did I reject the Data Scientist Job – defining a new role in a new industry. GE and AI in the PB data world. As well as 12 more TED talks on AI.

AI & Machine Learning News. 01, October 2018

AI Superpowers: China, Silicon Valley, and the New World Order by [Lee, Kai-Fu]U.S. should double A.I. funding, says the former head of Google China Kai-Fu Lee

As China becomes more active in artificial intelligence, the U.S. should double the amount it spends on research in the field, says investor and AI practitioner Kai-Fu Lee, who has worked for Google, Microsoft and Apple. The comments come after various parts of the U.S. government have made AI announcements, even as the U.S. overall lacks a formal AI strategy. Meanwhile, China introduced its plan last year: it’s aiming to be No. 1 in AI innovation by 2030.
“Double the AI research budget would be a good start, given that all other countries are so much farther behind U.S., and we’re looking for the next breakthrough in AI,” said Lee. Doubling funding could double the chances that the next big AI achievement will be made in the U.S
2018-09-29 00:00:00 Read the full story.

In The New AI World Order, China Will Soon Surpass US In R&D Investment – Kai-Fu Lee

For years, China vs US in AI has been a hotly contested topic with the matter taking on a geopolitical slant with national security coming in the center of debate. However, a new report published in IEEE Spectrum, Kai-Fu Lee, noted venture capitalist reiterated how in the new world order governed by AI, the real crisis will emerge from automation that wipes out whole job sectors, reshaping economies and societies in both nations. In a similar vein, AI exponent Andrew Ng discussed how in the coming years, there will be lots of cases where humans and machines will work side by side but the technology will also whitewash jobs and displace human labour.
Now Lee, whose new book — AI Superpowers: China, Silicon Valley, and the New World Order dwells on the world’s two AI titans explores the AI capabilities of China and the US and the battle for global dominance. But most importantly, it frames a positive rhetoric for China, which is slowly and surely edging closer to AI supremacy, thanks to the “speed, capital, China’s entrepreneurism and access to large amount of data”.
2018-09-27 10:43:28+00:00 Read the full story.

CloudQuant Thoughts… Mr Lee’s book tour is certainly getting him a lot of coverage. “It’s not China that is taking away the academic leaders; it’s the corporates,” Lee said. Is this a bad thing? We certainly need educators to teach the next generation but the US also needs to stay ahead of China in this most vital of fields. There is no doubt that Britain’s embrace of the scientific method, its scientific discoveries and the application of those discoveries to its industries (only ever so slightly ahead of its European competitors) was key to its domination of the world during the 16th to early 18th centuries. The US’s distance from WWII  meant it was ideally placed to help to re-build the devastated nations. That plus the collection of German rocket technology gave it the financial and scientific upper-hand during the latter half of the 20th century. AI and ML certainly seem to be the keys to power in the coming century. The Chinese government does have access to huge amounts of data on its citizens. US Corporations famed ability to be nimble and capable of taking advantage new technology may be the overall advantage.

Orchestrating data analytics to enhance the investor experience

The amount of data is growing exponentially. According to IDC, there were 16.3 zettabytes of information generated in 2017 alone; one zettabyte is 1 billion terabytes. However you cut it, that’s a huge number. One that is too large to comprehend. In simplistic terms, according to one industry professional “if every piece of data were a penny, it would cover the earth’s surface five times over”. Indeed, with Amazon and Apple both hitting the trillion dollar market cap mark, and Alphabet and Microsoft sitting at over USD900 billion, it is clear that the stock market values data as the most valuable resource, not oil or consumer products.
Against this growing tsunami, investment managers and service providers alike are looking for ways to ingest and make sense of it all. To find information that they can translate into insights and turn into knowledge, that if done correctly, could lead to improved business performance and enriched customer relationships.
2018-10-01 08:36:07+01:00 Read the full story.
CloudQuant Thoughts… An extremely interesting and detailed look into the role of Big Data, AI and ML at Investment Funds today .

What is Inside the Black Box of Artificial Intelligence? XAI – Explainable AI

Artificial Intelligence (AI) is surprising the mankind with newer and awe-inspiring outcomes. But with the surprises comes the concern of trust. There is a gripping ‘black box’ problem with artificial intelligence, if people don’t know how AI comes up with its outcomes and insights, they seldom trust the technology. The lack of trust can be attributed to the not so successful AI efforts for IBM, Watson for Oncology in particular. If the doctors knew how the platform Watson for Oncology came to its conclusions, the adoption rate would have been different. Since an interaction with something which is not understood can cause an anxiety and make the mind feel that it is losing control and hence not trust on that same.
It comes as no surprise that the US Department of Defence (DoD) is investing in Explainable AI (XAI) which will be very essential to understand the future war-fighters and trust the emerging generation of artificially intelligent machine partners. Efforts into XAI might soon lead new machine learning systems to explain their rationale, convey an understanding of how they behave in the future and characterize their strengths and weaknesses.
2018-09-26 00:01:48+05:30 Read the full story.
CloudQuant Thoughts… In our role at, writing AI and ML based models and setting them free on the market is simply not an option, one has to be able to understand “Why” the model is making its decisions. It appears this clarity is equally important in the medical and defense fields. Trust and accountability.

Why Did I Reject a Data Scientist Job? – Towards Data Science

Before diving in to tell you why I rejected a data scientist job, let us take a step back and try to answer another question — Why become a data scientist? Chances are you may have heard of the profession — Data Scientist was labeled by Harvard Business Review as the sexiest job of the 21st century and has been chosen as the best job in America, three years in a row according to Glassdoor. And more recently, IBM predicted demand for data scientists will soar 28% by 2020. All these attractive job prospects seem to point to a single direction where many people want to go after — and we all know — for some good reasons.
2018-09-30 11:43:25.416000+00:00 Read the full story.

CloudQuant Thoughts… Not surprisingly, most companies do not even know what they are looking for. Is it a Research Scientist, a Data Scientist, a Programmer, a Business Analyst? All of the above?

12 TED talks on Artificial Intelligence

  1. Daniel Dewey – The Long Term Future of AI
  2. Ray Kurzweil – How to create a mind
  3. Sam Spaulding – How AI is changing the way we view intelligence
  4. Henry Markram – A Brain in a SuperComputer
  5. George John – The Age of Artificial Intelligence
  6. Peter Bock – Emergence of Creativity in AI
  7. Hod Lipson – Robots that are “Self Aware”
  8. Boris Sofman – AI
  9. Alex Wissner – An equation for Intelligence
  10. Eric Horvitz – Making Friends with Artificial Intelligence
  11. Rudd Mattjeij – Artificial Intelligence
  12. Robin Hanson – The Next Great Era – Envisioning a Robot Society

2018-09-25 12:00:59+00:00 Read the full story.


ML Powers Discovery In GE’s 500 PB* Lake

Like most Fortune 50 firms, General Electric relies on an abundance of computer systems to power its enterprise. And like most firms that size, synching up and aligning the data emitted by different systems is a major challenge. But thanks to an innovative data discovery solution powered by machine learning, GE found a solution. GE’s Hadoop-based data lake contains 500 PB of data that originated from about 120 different systems. Data is sourced from a variety of ERP packages, accounting systems, and other applications, such as Ariba, Concur, and Even LinkedIn and Twitter data makes it into the lake for downstream sentiment analysis.
Getting actual value out of the data is a much tougher challenge. “Ingesting the data is the easiest part, I have the data from all of the sources. But now I have 130,000 entities in my data. Now, how do you identify all the relationships in all those entities?” There are things that customers know about their data, and there are things they don’t. “If you ingest so much data, you are ingesting to find insights in the data that you don’t know of,” Diwakar Goel continued. “That’s where you cannot rely on manual techniques to identify it and give you those insights. That’s why you use machine learning.”
GE found a potential solution to the problem in Io-Tahoe, a New York City-based data management startup that emerged from Centrica, a £28-billion company that owns British Gas and other subsidiaries. Io-Tahoe has developed a data discovery tool that uses patent-pending machine learning technology to determine the relationships between disparate pieces of data.
2018-09-25 00:00:00 Read the full story.
CloudQuant Thoughts… First… PB* –  A petabyte (PB) is 1,000 terabytes (TB) or 1,000,000 gigabytes (GB). GE is an enormous company and as such is one of the first to be dealing with this kind of volume of data.
Favorite Quote from this Week’s blogs…
“The job of the data scientist is to ask the right questions. If I ask a question like ‘how many clicks did this link get?’ which is something we look at all the time, that’s not a data science question. It’s an analytics question. If I ask a question like, ‘based on the previous history of links on this publisher’s site, can I predict how many people from France will read this in the next three hours?’ that’s more of a data science question.” ―Hilary Mason, Founder, Fast Forward Labs.


Cleaning and Preparing Data in Python – Towards Data Science

That boring part of every data scientist’s work

Data Science sounds like something cool and awesome. It’s pictured as something cool and awesome. It is a sexiest job of 21st century as we all know (I won’t even add the link to that article :D). All the cool terms are related to this field — Machine Learning, Deep Learning, AI, Neural Networks, algorithms, models… But all this is just a top of an iceberg. 70–80% of our work is data preprocessing, data cleaning, data transformation, data reprocessing — all these boring steps to make our data suitable for the model that will make some modern magic.
And today I would like to list all the methods and functions that can help us to clean and prepare the data. So what can be wrong with our data? A lot of things actually…
2018-09-30 23:23:17.782000+00:00 Read the full story.

Data And The Cloud: The Modern 21st Century Lawyer

We can then analyze digitized content using a variety of automated tools and platforms, which are powered by AI and machine learning technologies. The beauty of machine learning is that we can use it to build a model or algorithm, then continuously improve it over time. So, you start with a good baseline, but it only gets better and more accurate from there.
That explains why the demand for tech-savvy attorneys and lawyers has grown considerably in recent years.
2018-09-27 23:18:49+00:00 Read the full story.


Amazon scientist explains how Alexa resolves ambiguous requests

In a blog post today, Vishal Naik explained how Alexa leverages multiple neural networks — layered math functions that loosely mimic the human brain’s physiology — to resolve ambiguous requests. The work is also detailed in a paper (“Context Aware Conversational Understanding for Intelligent Agents with a Screen“) that was presented earlier this year at the Association for the Advancement of Artificial Intelligence.
“If a customer says, ‘Alexa, play Harry Potter,’ the Echo Show screen could display separate graphics representing a Harry Potter audiobook, a movie, and a soundtrack,” he explained. “If the customer follows up by saying ‘the last one,’ the system must determine whether that means the last item in the on-screen list, the last Harry Potter movie, or something else.”
2018-09-28 00:00:00 Read the full story.


Adobe Relaunches CX Platform

Adobe, Microsoft, and SAP founded the Open Data Initiative, an alliance that aims to make data silos a complete thing of the past. The Open Data Initiative promotes the development of tools that provide a seamless flow of customer data; everything from behavioral and transactional to financial and operational data comes together with one data model, making it possible to have a comprehensive, real-time view of customer data across multiple devices.
Adobe Customer Experience (CX) Platform uses data science plus Adobe’s Sensei analytics artificial intelligence engine to synthesize all customer data in one place. So Adobe has quite a bit of investment in this whole initiative. New Virtual Analyst: Powered by Adobe Sensei, deep AI and machine learning will enable enterprises to unlock more value in the mountains of data they have. The AI will break out critical insights buried deep in data–without the user even having to ask. The cognitive analyst will get more intelligent over time as it learns from user behaviors and delivers more relevant insights.
2018-09-26 00:00:00 Read the full story.

Microsoft teams up with Adobe and SAP on Open Data Initiative to link data across their products

Modern enterprise technology generates a ton of valuable data, but putting that data together can be quite tricky. Microsoft introduced a new industry partnership with Adobe and SAP Monday at Ignite 2018 that aims to bring customer data together into one package running on Azure.
2018-09-24 14:11:07-07:00 Read the full story.

Volkswagen strikes deal with Microsoft to build cars connected to the cloud

Volkswagen and Microsoft have struck a deal that will see the car-maker embed internet services into its vehicles with the software giant’s cloud technology, the companies announced Friday.The partnership has been set up to create the “Volkswagen Automotive Cloud”, which will see all future digital services in Volkswagen’s cars facilitated by Microsoft Azure, a cloud computing service.
From 2020, Volkswagen will integrate more than 5 million Volkswagen-branded vehicles per year to an internet of things network. The move forms part of a wider $4bn push from the German company to boost its digital transformation by 2025.Volkswagen is taking steps for a digital transformation that it hopes will serve as the technological basis for an “industrial automotive cloud”.
2018-09-28 00:00:00 Read the full story.


Weekly Selection — Sep 28, 2018 – Towards Data Science

  • Wikipedia Data Science: Working with the World’s Largest Encyclopedia
  • Illustrated Guide to LSTM’s and GRU’s: A step by step explanation
  • Neural Networks to Predict the Market
  • 5 Reasons why Businesses Struggle to Adopt Deep Learning
  • How to rapidly test dozens of deep learning models in Python
  • Convolutional Neural Networks for Beginners: Practical Guide with Python and Keras
  • Multi-Class Text Classification Model Comparison and Selection
  • Here’s how you can get a 2–6x speed-up on your data pre-processing with Python
  • Why do I Call Myself a Data Scientist?
  • See Robot Play: an exploration of curiosity in humans and machines

2018-09-28 12:53:12.232000+00:00 Read the full story.


Enriching customer relationships in financial services: the role of automation

Since every major step forward in process efficiencies, in any industry, entails a moral consideration, it’s no surprise that there may be some hesitation around its adoption in the financial services sector. My colleague, Jason Bell, recently discussed the pressing need for banks to transform into technology companies in his blog, ‘The opportunity no bank can ignore’.
Jason discussed the emphasis often given to the year 2020 as a watershed year, pointing out how close it is. It brings many observations about baking futures into sharp focus, such as this observation from the Accenture article Intelligent Automation in Financial Services: “70% of financial services executives believe artificial intelligence will completely or significantly change their organization by 2020.” Without doubt, Artificial Intelligence (AI) and Robotic Process Automation (RPA) are powerful efficiency drivers essential to the forward momentum of digital transformation. The moral side-bar is the impact these technologies may have on jobs. In 2013, an Oxford University research paper, The Future of Employment: How susceptible are jobs to computerisation?, predicted that up to 47% of workers in the US economy were at a high risk of being replaced by robots in the medium term.
2018-09-27 16:50:21 Read the full story.

Bringing the power of Windows 10 to the Robot Operating System – Windows Experience Blog

People have always been fascinated by robots. Today advanced robots are complementing our lives, both at work and at home. Warehouse robots have enabled next-day deliveries to online shoppers, and many pet owners rely on robotic vacuums to keep their floors clean. Industries seeing benefits from robots are as diverse as manufacturing, transportation, healthcare and real estate. As robots have advanced, so have the development tools. We see robotics with artificial intelligence as universally accessible technology to augment human abilities.
Windows has been a trusted partner of robotic and industrial systems for decades. With ROS for Windows, developers will be able to use the familiar Visual Studio toolset along with rich AI and cloud features. We’re looking forward to bringing the intelligent edge to robotics by bringing advanced features like hardware-accelerated Windows Machine Learning, computer vision, Azure Cognitive Services, Azure IoT cloud services, and other Microsoft technologies to home, education, commercial, and industrial robots.
2018-09-28 00:00:00 Read the full story.


Microsoft brings Robot Operating System to Windows 10

In recent years, the robotics industry has experienced outsized growth. It’s expected to be worth almost $500 billion by 2025, and judging by recent funding rounds, investors are optimistic about the future. Warehouse robotics company GreyOrange raised $140 million for its platform in early September; in June, Bossa Nova scooped up $29 million in July for its store inventory robots and Starship Technologies secured $25 million for its fleet of automated delivery carts.
One thing many of those startups’ machines share in common is Robot Operating System (ROS), open source robotics middleware originated by Willow Garage and Stanford’s Artificial Intelligence Laboratory that provides low-level device control, hardware abstraction, and other useful services. Previously, ROS was experimentally supported on Windows by the community. (As of September 2018, Core ROS had been ported to Windows.) But today, Microsoft debuted an official — albeit “experimental” — build for Windows 10.
2018-09-28 00:00:00 Read the full story.


University spin-out raises cash for tech to help robots avoid crashes

A company spun out of Imperial College has raised $5m (£3.8m) from Amadeus Capital Partners and other investors to build more advanced navigation systems for drones and robots. SLAMcore develops artificial intelligence (AI) technology for robots to help prevent crashes. Its system – known as simultaneous localisation and mapping (SLAM) – allows robots to understand and navigate unfamiliar surroundings such as inside buildings or dense urban areas.
Nearly all autonomous vehicles or robots, from driverless cars to robot vacuums, use SLAM technology in some way, and a huge variety of different systems are being developed by big and small technology companies. BIS Research expects the global market for SLAM technology to be worth more than $8bn by 2027.
Owen Nicholson, chief executive, said: “We’re really trying to democratise robots. It shouldn’t be the reserve of just a handful of tech giants because that’s not good for innovation.”. SLAMcore’s technology would allow robotics companies to “get on with coming up with crazy new ideas”.
2018-09-27 00:00:00 Read the full story.

RBC Capital and Orbital Insight Ink Alt Data Deal

Orbital Insight, the leader in geospatial analytics, and RBC Capital Markets, the corporate and investment banking arm of Royal Bank of Canada (RBC), today announced a global partnership giving RBC Capital Markets access to Orbital Insight’s Consumer and Energy analytics products.
RBC Capital Markets will use this data to further advance its equity research products, taking advantage of the timeliness, objectivity and scale of geospatial analytics. By using artificial intelligence to automatically analyze data like satellite imagery, Orbital Insight is able to detect and track changes on the ground over time. Signals monitored include retailer parking lot car counts and crude oil storage tanks, among others.
2018-09-28 18:07:28+00:00 Read the full story.

Why Convolutional Neural Networks Are The Go-To Models In Deep Learning

Over the years, research on convolutional neural networks (CNNs) has progressed rapidly, however the real-world deployment of these models is often limited by computing resources and memory constraints. What has also led to extensive research in ConvNets is the accuracy on difficult classification tasks that require understanding abstract concepts in images.
Another reason why CNN are hugely popular is because of their architecture — the best thing is there is no need of feature extraction. The system learns to do feature extraction and the core concept of CNN is, it uses convolution of image and filters to generate invariant features which are passed on to the next layer. The features in next layer are convoluted with different filters to generate more invariant and abstract features and the process continues till one gets final feature / output (let say face of X) which is invariant to occlusions. But one of the reasons why researchers are excited about deep learning is the potential for the model to learn useful features from raw data. Now, convolutional neural networks can extract informative features from images, eliminating the need of traditional manual image processing methods.
2018-09-25 09:25:09+00:00 Read the full story.

Instant Data Science

If you have skimmed through the high tech job listings lately, you’ve surely noticed the demand for data scientists. Although there is no consensus on the exact job definition, many companies want data experts and are willing to pay a high price for the right candidates. This article explores why good data scientists are hard to find, and explains how the shortage may soon be alleviated thanks to… data science!
2018-09-27 00:00:00 Read the full story.


Daring to Head Towards an AI-Powered World

Some of us perceive Artificial Intelligence as the technology of a haunting future, just as projected in science fiction movies, The Matrix or Ex Machina. In the real world, it is changing lives in brilliant ways, beyond areas we dare to imagine.
AI has taken over our world. Of course not in an apocalyptic way, because let’s be realistic, even thinking this is ridiculous! Most people do not realize, even today, when they interact with an AI. For example, with chat bots. I once had a friend ask me why do local businesses invest in 24/7 customer chat teams for websites.
2018-09-28 12:31:15.398000+00:00 Read the full story.


Data Warehouses, Data Marts, Operational Data Stores, and Data Lakes: What’s in a Name?

Many organizations nowadays are struggling with finding the appropriate data stores for their data. Let’s zoom in on some key data structures to facilitate corporate decision making by means of business intelligence. More specifically, let’s look at data warehouses, data marts, operational data stores, data lakes, and their differences and similarities.
2018-09-26 00:00:00 Read the full story.


Alexa, Tell Me About My Advisory Business

digital financial services, have a new tool to call upon when it comes to tracking their business metrics. Envestnet Intelligence for Financial Institutions, a new data intelligence tool, was announced Tuesday at FinovateFall 2018 in New York.
Machine learning and natural language processing—two of the most commonly employed technology subsets within the field of artificial intelligence—are being applied to the massive amounts of business data Envestnet collects from, and on behalf of, its institutional customers. A homegrown data analytics engine then powers the platform, which can be accessed from a user’s desktop, mobile and Amazon Alexa-enabled devices. It’s not the first time Envestnet has voice-enabled one of its products with Alexa; the firm added this feature to its Envision IQ product in May.
2018-09-26 15:52:04-04:00 Read the full story.


How Edge Computing Enables Real-Time Decision Making (Video) – Will Ochandarena

As more and more companies try to implement use cases that need to do things in real time, make decisions in real-time, collect in real-time, show dashboards in real-time, organizations have to build an architecture that’s able to collect data in real-time, said Will Ochandarena, senior director, product management, MapR Technologies during Data Summit 2018.
“We’re talking every week with different oil companies and factories that are trying to improve their yields, productivity, and quality of their product output and it’s architectures like this that make it possible,” Ochandarena said.
2018-09-25 00:00:00 Read the full story.


Data Science Hacks No One Talks About But Are A Must In Your Toolkit

With the data revolution in full swing, there is more information on the internet than a human can remember and process in his/her lifetime. Data Science is a demanding platform, where every forward looking enterprise and startup wants to increase their productivity with the help of intelligent systems. It is an interdisciplinary platform that involves numerous techniques and skills such as, analysis, programming, math and statistics. Now, it is commonly believed that a person with a hacker mindset can come up with an easier solution compared to an orthodox approach. Let us look at some of the little-known hacks in data science field which aren’t extensively talked about.

  • Hacker Mindset
  • Data Cleaning Tricks
  • Domain Knowledge
  • Never say “No More Learning”
  • Cheat SheetsHacker Mindset
  • Data Cleaning Tricks
  • Domain Knowledge
  • Never say “No More Learning”
  • Cheat Sheets

2018-09-25 05:25:19+00:00 Read the full story.

This Mumbai Startup Is Attempting To Introduce Level 2 Autonomy In India With Its Innovative Solution

AI-powered autonomous technology is poised to see enormous growth over the coming years, however tech development is mostly concentrated in North America and China. In a first, Pinaki Laskar, co-founder of Mumbai-based, Fisheyebox, has developed Drivex.AI — dubbed as an all-in-one-solution for an intelligent drive system. In an exclusive by Express Computer, Laskar talked about the Project DriveX that can interface with automotive standard drive-by-wire platform, and turns it into a driverless unit. The news further stated that the startup has also successfully tested its autonomous technology in Maruti Celerio.
2018-09-28 11:45:12+00:00 Read the full story.

Managing a Data Warehouse in the Cloud: 5 Challenges

One of the most important shifts in data warehousing in recent times has been the emergence of the cloud data warehouse. Previously, setting up a data warehouse required a huge investment in IT resources to build and manage a specially designed on-premise data center. Now, several cloud computing vendors offer data warehousing functions as a service (DWaaS), accessible via an Internet connection. This model negates the costly capital expenditure and management required for an on-premise data warehouse.
The availability of cloud data warehouses makes data warehousing much more accessible to a wider range of companies. However, before you go rushing into choosing a vendor and getting set up, first understand that managing a data warehouse in the cloud presents a whole new set of challenges, regardless of whether you’ve managed an on-premise setup before. Five of the main challenges and some recommended solutions are outlined below to help you better prepare for managing a data warehouse in the cloud.
2018-09-26 00:00:00 Read the full story.


Why You Need to Trust Your Data

There’s something that often gets lost in discussions about artificial intelligence and advanced analytics: the importance of the data. Having good, clean data is absolutely essential, but all too often, companies lack trust in that critical resource, which can lead business leaders to make bad decisions or resort to following gut instincts.
No matter how good your analytics are, you’re not getting anywhere if you can’t trust your data. The goal for many companies when constructing predictive analytics is to get the data as clean as possible, a process that studies show can consume up to 80% of a data scientist’s time. But even if the data is 100% true, your model may give the wrong predictions if the data doesn’t accurately reflect the thing you’re trying to predict.
2018-09-26 00:00:00 Read the full story.


Is Artificial Intelligence Replacing Jobs In Banking?

Over the past 12 months, the banking industry has become increasingly excited about AI. Virtually every leading consultancy has published research on the impact AI will have on the sector and investment continues to pour into developing innovative solutions. But, alongside all the buzz comes the inevitable concern that the implementation of this technology will reduce the need for actual human workers.
2018-09-26 00:00:00 Read the full story.


10 must watch movies on Data Science and Machine Learning

Data science and machine learning are powerful technologies innovating the world in ways that sometimes seem straight out of a sci-fi film. Today’s machines are not just capable of tedious tasks, but also using complex mathematics to figure out how to chart a path for a rocket to follow or making weather predictions based on historical data.
What better platform to explore the magic of data science and machine learning than film? We’ve rounded up 10 of the best data movies:
2018-10-01 15:52:23+00:00 Read the full story.


Microsoft commits $40M over 5 years to AI for Humanitarian Action initiative

Microsoft is investing in a new program employing its vast artificial intelligence resources to deal with humanitarian crisis. Set against the backdrop of Hurricane Florence pummeling the Carolinas last week, Microsoft announced in conjunction with the United Nations General Assembly meeting on Monday morning that it is pledging $40 million over five years for a new AI for Humanitarian Action initiative. The program will focus on using AI to aid in four areas: disaster recovery, children’s needs, protecting refugees and displaced people and human rights.
2018-09-24 13:00:31-07:00 Read the full story.


The State of Machine Learning in Business Today

Artificial Intelligence (AI), Machine Learning, and Deep Learning are all topics of considerable interest in news articles and industry discussions these days. However, to the average person or to senior business executives and CEO’s, it becomes increasingly difficult to parse out the technical differences which distinguish these capabilities. Business executives want to understand whether a technology or algorithmic approach is going to improve business, provide for better customer experience, and generate operational efficiencies such as speed, cost savings, and greater precision. Authors Barry Libert and Megan Beck have recently astutely observed that Machine Learning is a Moneyball Moment for Companies.
I met last week with Ben Lorica, Chief Data Scientist at O’Reilly Media, and a co-host of the annual O’Reilly Strata Data and AI Conferences. O’Reilly recently published their latest study, The State of Machine Learning Adoption in the Enterprise. Noting that “machine learning has become more widely adopted by business”, O’Reilly sought to understand the state of industry deployments on machine learning capabilities, finding that 49% of organizations reported they were exploring or “just looking” into deploying machine learning, while a slight majority of 51% claimed to be early adopters (36%) or sophisticated users (15%).
2018-09-27 09:08:59+00:00 Read the full story.


Privacy at an inflection point: Why the time has come for meaningful U.S. regulation

As the privacy bus teeters at the edge of a steep road, the U.S. Congress and President seem to be asleep at the wheel. While we witness fiery rhetoric at televised hearings featuring high tech CEO’s and although a few members of Congress have put forth credible proposals to protect personal data, very little actual progress has been made to date on concrete consumer protection legislation. This paralysis at the federal level benefits neither companies nor consumers, as the time has come to craft new laws for an economy increasingly driven by data profiling and artificial intelligence.
2018-09-28 13:00:20-07:00 Read the full story.


7 examples of Big Data Retail Personalization

Big data is a top trending buzzword. But, unlike overused buzzwords such as ‘omnichannel marketing’ or ‘growth hacking’, big data is very underhyped. According to IBM, 62% of retailers report that the use of big data is giving them a serious competitive advantage. Knowing what your customer wants and when they want it can be available at your fingertips with big data; all you need are the right tools and processes in place to make use of it. Let’s explore 7 innovative examples of big data personalization in retail for some inspiration.
2018-09-26 11:00:05+00:00 Read the full story.

The Power of Personalization: Cramer’s ‘Mad Money’ Recap (Thursday 9/27/18)

Investing is all about the search for great stories, Jim Cramer told his Mad Money viewers on Thursday. But a good theme is not enough, you also need to invest at the right time. That’s why Cramer regularly visits Silicon Valley, to hear first-hand what the next big themes will be. This week, Cramer’s in San Francisco for the Dreamforce event, and visiting companies and CEOs in Silicon Valley.
The biggest theme this trip? Personalization. Companies that know who their customers are and what they want before they do are able to dominate their industries, Cramer said, and the companies providing those companies with the tools to personalize are the winning investments.
2018-09-27 22:39:58-04:00 Read the full story.


Assessing Annotator Disagreements in Python to Build a Robust Dataset for Machine Learning


Having recently completed an MSc in Data Analytics and then working closely with a charity in machine learning, what is only now striking me as troubling about the state of formal education is the distinct under-focus on the bottom three building blocks, and an over-focus on the top two blocks. Despite it being troubling, the reason is clear: students aren’t attracted to the gritty pre-machine learning phase. But the fact remains that without an understanding of how to work on what might be less exciting, it simply won’t be possible to build a robust, strong, and reliable machine learning product. (VIDEO LINK)
2018-09-29 19:21:18.535000+00:00 Read the full story.


NHS to trial Uber-style location service to match up patients, porters and equipment

The NHS is to trial a new Uber-style geolocation service to match up patients, porters and equipment to get people around hospitals more quickly. Currently, the complexity and vastness of hospital complexes make it difficult to match up porters with patients, meaning they are often left waiting to be taken to x-rays or theatre.
But the new system designed by the University of Oxford and tech company Navenio Ltd, will make it easy to locate staff and match them to nearby patients, in the same way as the taxi app. It is hoped the project will increase staff productivity to 96 per cent and cut costs by up to 35 per cent and will be initially tested at hospitals in the Oxford area.
2018-09-28 00:00:00 Read the full story.


Why tech VCs invest in people, not ideas

Venture capitalists are regarded in Silicon Valley as the heroes of many of technology’s startup successes, bringing in money and expertise when entrepreneurs needed to develop original ideas or expand into larger markets. But it still comes down most of all to the entrepreneurs more than their ideas.
“What I’m really looking for is in investing more in the people than in the idea, because startups can always pivot,” said Patrick O’Reilly. “On the developer operations side, I’m looking for people using AI to get away with repetitive tasks,” O’Reilly said. “I would love to see someone have a system where it’s like, ‘Hey, we’ve noticed 90 times this week this guy’s done this exact same thing, 99 percent the same way.’ Let’s automate that away. We’ve been really good in the space to treat infrastructure like code, and be able to tear things up.”
2018-09-27 00:00:00 Read the full story.

The Day Of The Machine Is Here: Are We Human Enough To Seize It?

You’re an executive, an employer, or maybe an up-and-comer in your organization, but either way, you have surveyed the moment and you know that big things are poised to happen. Unlike the big things of the past — the internet or mobile — the change won’t be the result of consumers embracing a shiny new object or buying into a piece of Apple hardware that other manufacturers will swiftly imitate. The change that’s coming will be more diffuse, more comprehensive, and at once harder to see coming in specifics yet impossible to miss in general.
2018-09-27 17:36:22+00:00 Read the full story.

First Fully Automated Indoor Farm Being Built In Ohio

The next time you shop for cherry tomatoes at Whole Foods or another retailer, you may end up buying some grown in an indoor, controlled environment outfitted with the latest robotic technology. Ohio will get the first fully automated indoor farm in the United States. 80 Acres Farms plans to build one in Hamilton, a suburb of Cincinnati, by the end of the year. The farm will have grow centers for greens, such as herbs and kale, and will supply produce to multiple retailers and distributors.
The indoor farm in Hamilton will include artificial intelligence, robotics, sensors and other tools to monitor the produce around the clock.
2018-09-25 00:00:00 Read the full story.


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UK data science can lead a global privacy-first mantra

Privacy breaches present a repeating alarm we can’t ignore. On Tuesday, the day before major US-based technology companies such as Apple and Twitter testified at the Senate, Facebook discovered a breach that affected 50m users’ accounts and their linked applications.
While the Facebook news wasn’t announced until Friday, the purpose of the Senate hearing was acutely apt. How do the major tech firms exercise their vast power in a way that protect…
2018-09-30 00:00:00 Read the full story.


Big Data, the Next Generation: Faster, Easier, Smarter

A lot has happened since the term “big data” swept the business world off its feet as the next frontier for innovation, competition and productivity. Hadoop and NoSQL are now household names, Spark is moving towards the mainstream, machine learning is gaining traction and the use of cloud services is exploding everywhere. However, plenty of challenges remain for organizations embarking upon digital transformation, from the demand for real-time data and analysis, to need for smarter data governance and security approaches. Download this new report today for the latest technologies and strategies to become an insights-driven enterprise.
2018-09-26 00:00:00 Read the full story.

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