Industry News

Industry News

machine Learning and AI news covering Nvidia’s Use of Artificial Intelligence to Render Virtual Worlds in Real Time, Advances in Financial Machine Learning Slideshow, Seven EU countries filing GDPR complaints against Google, The dawn of self-driving companies, AI’s Cybersecurity Threat, and Alibaba’s speech recognition algorithm can isolate voices in noisy crowds.

AI & Machine Learning News. 03, December 2018

Nvidia Uses Artificial Intelligence to Render Virtual Worlds in Real Time

Nvidia announced that AI models can now draw new worlds without using traditional modeling techniques or graphics rendering engines. This new technology uses an AI deep neural network to analyze existing videos and then apply the visual elements to new 3D environments.
Nvidia claims this new technology could provide a revolutionary step forward in creating 3D worlds because the AI models are trained from video to automatically render buildings, trees, vehicles, and objects into new 3D worlds, instead of requiring the normal painstaking process of modeling the scene elements.
2018-12-03 00:00:00 Read the full story (Tom’s Hardware).
2018-12-03 00:00:00 Read the full story (Verge).
CloudQuant Thoughts… NVIDIA are regularly top of our summary and the reason why is clear to see, all of their research is visually appealing. This is not surprising for a company that was built on the core product of making video games prettier. However, we can all learn a lot from Nvidia’s PR. Image is important, communicating your message in a clear and concise manner is important.

Advances in Financial Machine Learning

In a series of nine presentation slide sets (Lectures 1-9 of 10) on “Advances in Financial Machine Learning”, Marcos Lopez de Prado provides part of Cornell University’s ORIE 5256 graduate course at the School of Engineering (“Special Topics in Financial Engineering V”). The course description includes: “Machine learning (ML) is changing virtually every aspect of our lives. As it relates to finance, this is the most exciting time to adopt a disruptive technology that will transform how everyone invests for generations [see the chart below]. Students will learn scientifically sound ML tools used in the financial industry.”
2018-11-29 17:23:02+00:00 Read the full story.
CloudQuant Thoughts… A very nice slide set, well worth perusing!

Seven EU countries file GDPR complaints against Google

Netherlands, Poland, Czech Republic, Greece, Slovenia, Sweden, and Norway allege Google’s location tracking violates GDPR
Consumer groups in Europe have asked privacy regulators to take action against tech-titan Google for deceptively tracking the movements of millions of Europeans, Reuters reports. In total seven consumer groups have filed complaints. Speaking on behalf of the countries’ consumer groups, the European Consumer Organisation (BEUC) claimed Google’s practices lack ‘valid legal ground for processing the data in question’. BEUC alleges that Google uses “deceptive” methods – but exactly what practices is it referring to?
2018-11-28 00:00:00 Read the full story.
CloudQuant Thoughts… For me this is the biggest story of the week, month, year. We all know the play off between AAPL and GOOG is that one charges a fortune but does not sell your data out from under you whereas the other charges you very little but makes up the deficit (and more) by mining you harder than a bitcoin farm (circa Dec 2017) but how aware are ordinary members of the public that Google tracks their every move.

The dawn of self-driving companies

If you’ve already imagined how profoundly self-driving vehicles will change our lives, think of what self-driving companies could do. I’m talking about self-executing enterprise software that, when mature, could enable businesses to virtually run themselves. Autonomous companies may be less sexy than autonomous cars, but their impact on society will be just as significant. Autonomous vehicle systems are ranked from zero (no automation) to five (fully self-driving). For example, Tesla’s Autopilot, which requires drivers to keep at least one hand on the steering wheel, is Level 2. To borrow this scale, today’s business software would rank somewhere between zero and one — the power-steering stage, let’s call it. Most current B2B software is workflow-based; that is, software that helps organize and facilitate routinized tasks. Salesforce, the cloud computing company, for example, is largely a workflow-driven software solution. To get paid, sales reps of enterprises using Salesforce have to input their activities, which allows supervisors to monitor their work and manage the sales pipeline more efficiently.
This type of business software has unlocked enormous productivity, and most multi-billion-dollar B2B-software companies today are some form of workflow solution. Over the next decade, I believe that these impressive results will be dwarfed by the value created when AI-driven business applications attain Level 4/5 autonomy.
2018-12-01 00:00:00 Read the full story.
CloudQuant Thoughts… For no other reason than I really like that term “self driving companies”. You heard it here first!

Does AI Pose More of a Threat to Cybersecurity Than We Think?

We often think about artificial intelligence (AI) in terms of the benefits it can provide by helping us complete tasks more efficiently. It’s important to remember, though, that this technology can be used just as easily for malicious ends. Today, both cybersecurity experts and cybercriminals are using AI. Could it pose more of a cybersecurity threat than we think?
Because AI can learn on its own and use that knowledge to complete tasks autonomously, it can help us complete work more efficiently, more cost-effectively, more accurately and with less hands-on effort. Those benefits apply to virtually every sector. They also apply to cyber attacks and other security threats. Cybercriminals can use AI to automate aspects of their attacks, enabling them to launch attacks more quickly, at a greater scale and a lower cost. They may also be able to pinpoint their targets more precisely.
2018-11-28 00:00:00 Read the full story.
CloudQuant Thoughts… There is no doubt that, whilst we focus on what AI can do the the benefit on humankind, there are those out there who are twisting it in devious ways!

Alibaba’s speech recognition algorithm can isolate voices in noisy crowds

Chinese conglomerate Alibaba is one of the world’s largest ecommerce companies, but it’s increasingly turning its attention to artificial intelligence (AI). In March 2017, it launched an AI services division for health care and manufacturing, and in September its public cloud division — Alibaba Cloud — unveiled plans to set up a dedicated subsidiary and produce a self-developed AI inference chip that could be used for logistics and autonomous driving. Alibaba has its fingers in plenty of AI pies, needless to say. And during a presentation at NeurIPS 2018 in Montreal this morning, it delivered an update on those cross-company efforts.
“We’re solving … scenarios [with] unseen difficulties,” said Rong Jin, dean of the Alibaba Institute of Data Science. “AI together with innovation [is helping] to solve some interesting challenges.” One of those challenges is speech recognition in noisy environments, like a crowded subway system or congested convention center. Alibaba’s solution is part hardware, part software: a far-field microphone array and sophisticated deep learning algorithms that isolate voices in a crowd, drastically reducing error rate.
2018-12-02 00:00:00 Read the full story.
CloudQuant Thoughts… Why is it that almost every AI story that comes out of China fills me with dread! But I guess this is no more scary than the visual Microphone…

Below the Fold…

Three Machine Learning Tasks Every AI Team Should Automate

With the war on AI talent heating up, the new “unicorns” of Silicon Valley are high-performing data scientists. Although as recently as 2015 there was a surplus of data scientists, in the most recent quarter there was a 150,000 deficit. This quant crunch will only grow deeper as the gap between the demand for these experts in developing machine learning models is not met with the supply from graduate programs. How do leading companies take steps to mitigate the damage of the quant crunch on their ability to earn a return on machine learning and AI investments? They empower the experts that they do have with a combination of tools and techniques that automate as much of the tedious components of the modeling process as possible.
It is a relatively simple formula: automate tasks that do not benefit from domain expertise, thereby freeing your team up to spend time on the tasks that do. Below is a framework for considering which tasks are automatable and the beginning of a playbook for how to do so most efficiently and effectively.
2018-11-30 00:00:00 Read the full story.

Prevalence-Induced Behavior and AI Self-Driving Cars

…There was an interesting phenomenon that overcame me during the first day or two of driving in this rather quiet town. When I observed a car approaching me, doing so via my rear-view mirror, I would instinctively start to react as though the car was going to be a pushy driver. I did this repeatedly. Yet, by-and-large, the upcoming driver did not try the crazed pushiness dance that I was used to in Los Angeles. I wasn’t even aware that I was reacting until a passenger in my car noticed that I was slightly tensing up and moving forward when it wasn’t necessary to do so (I was trying to create a gap between me and the assumed pushy driver behind me, a form of defensive maneuver in reaction to a pushy driver).
What was happening to me? I was likely experiencing prevalence-induced behavioral change. That’s a bit of jargon referring to one of the expanding areas of exploration about human judgment and social behaviors.
2018-11-26 20:45:26+00:00 Read the full story.


Trying to keep up with AI research papers can feel like an exercise in futility given how quickly the industry moves. If you’re buried in papers to read that you haven’t quite gotten around to, you’re in luck.
To help you catch up, we’ve summarized 10 important AI research papers from 2018 to give you a broad overview of machine learning advancements this year. There are many more breakthrough papers worth reading as well, but we think this is a good list for you to start with.

  • Universal Language Model Fine-tuning for Text Classification
  • Obfuscated Gradients Give a False Sense of Security: Circumventing Defenses to Adversarial Examples
  • Deep Contextualized Word Representations
  • An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling
  • Delayed Impact of Fair Machine Learning
  • World Models
  • Taskonomy: Disentangling Task Transfer Learning
  • Know What You Don’t Know: Unanswerable Questions for SQuAD
  • Large Scale GAN Training for High Fidelity Natural Image Synthesis
  • BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

2018-11-27 18:42:38+00:00 Read the full story.

Dremio Donates Fast Analytics Compiler to Apache Foundation

Dremio has donated the Gandiva Initiative — a LLVM-based execution kernel designed to speed up analytical workloads – to the Apache Software Foundation, where it will become available to anybody who wants it as part of the Apache Arrow project.
Dremio spent considerable resources developing Gandiva Initiative for Apache Arrow, which it originally released last month as part of Dremio 3.0. Dremio is a data-as-a-service offering that uses the in-memory columnar Apache Arrow data format to speed up and simplify how data analysts and data scientists access a wide range of data sources.
According to Dremio, some queries and operations, when run through the Gandiva compiler, can execute 100 times faster. The software has already been incorporated into several data science tools, including Pandas, which is popular with Python developers, as well as Apache Spark. Nvidia is also using it in its recently announced RAPIDS initiative to accelerate machine learning adoption.
2018-11-28 00:00:00 Read the full story.

Top Machine Learning GitHub Repositories from November 2018

Quite a collection this month. I found the GAN Dissection repository quite absorbing. I’m currently in the process of trying to replicate it on my own machine – should be quite the ride. I’m also keeping an eye on the ‘Reverse Engineering a Massive Neural Network’ thread as the ideas spawning there could be really helpful in case I ever find myself in that situation.
Open AI’s Deep Reinforcement Learning Resource
NVIDIA’s WaveGlow
BERT as a Service
Python Implementation of Google’s ‘Quick Draw’ Game
Visualizing and Understanding GANs
Reddit Discussions
Why Gradient Descent is Even Needed in the First Place
Reverse Engineering a Massive Neural Network
Debate on TensorFlow 2.0 API
Reinforcement Learning with Prediction-Based Rewards
Landing that First Data Scientist Job
2018-12-03 09:27:01+05:30 Read the full story.

Super-Intelligent AI Paperclip Maximizer Conundrum and AI Self-Driving Cars

Paperclips. They quietly do their job for us. Innocent, simple, nondescript. You probably have paperclips right now somewhere near you, doing their duty by holding together a thicket of papers. In the United States alone there are about 11 billion paperclips sold each year. That’s about 34 paperclips per American per year. You likely have some straggler paperclips in your pocket, your purse, in the glove box of your car, and in a slew of other places. Little did you know the danger you face.
There is a paperclips apocalypse heading our way. Locking your doors won’t stop it. Tossing out the paperclips you have in-hand won’t help. Moving to a remote island will not particularly increase your chances of survival. Face the facts and get ready for the dawning of the paperclips war and the end of mankind. What am I talking about? Have I gone plain loco?
I’m referring to the everyday-person obscure but also semi-popular in AI “paperclip maximizer” problem. It goes somewhat like this.
2018-11-29 21:45:14+00:00 Read the full story.

LogicAI and Kaggle team up on Kaggle Days events in 2019 and beyond!

ast year, a group of Kaggle fans from LogicAI had the idea of an offline event dedicated to serving the data science community. Their efforts were so inspirational that we reached out and asked if they’d like to shape future “Kaggle Days” events together. After the success of the first event in Warsaw last year, we’ve decided to officially partner with LogicAI for additional Kaggle Days events and meetups—the first of which will be held in January 2019, in Paris!
Why Kaggle Days?  The Kaggle community has over 2 million of users from all over the world. As a fully remote team ourselves, we know the importance of getting together in person on a regular basis. Events like Kaggle Days can deepen relations among existing Kagglers, and entice more data scientists to engage with us in the future.
2018-11-27 00:00:00 Read the full story.

Digit Recognizer – Introduction to Kaggle Competitions with Image Classification Task (0.995)

In today’s article, I am going to show you how to master machine learning skills by participating in Kaggle data science competitions. We will solve a simple written digits recognition task (MNIST) and compare our results with others’. You will be challenged to master your analytical and coding skills in order to succeed in a highly competitive Kaggle community.
Kaggle offers 5 main functionalities i.e Competitions, Datasets, Kernels, Discussion and Learn. Feel free to check all of them but in this article, we will focus only on the Competitions.
2018-12-02 23:20:03.078000+00:00 Read the full story.

Advances in few-shot learning: a guided tour – Towards Data Science

Few-shot learning is an exciting field of machine learning right now. The ability of deep neural networks to extract complex statistics and learn high level features from vast datasets is proven. Yet current deep learning approaches suffer from poor sample efficiency in stark contrast to human perception — even a child could recognise a giraffe after seeing a single picture. Fine-tuning a pre-trained model is a popular strategy to achieve high sample efficiency but it is a post-hoc hack. Can machine learning do better?
Few-shot learning aims to solve these issues. In this article I will explore some recent advances in few-shot learning through a deep dive into three cutting-edge papers:

  • Matching Networks: A differentiable nearest-neighbours classifier
  • Prototypical Networks: Learning prototypical representations
  • Model-agnostic Meta-Learning: Learning to fine-tune

I will start with a brief explanation of n-shot, k-way classification tasks which are the de-facto benchmark for few-shot learning.
2018-11-30 22:41:32.878000+00:00 Read the full story.

Redtail Technology Survey Reveals Financial Advisory Firms Are More Tech Empowered Than Industry Thinks

Redtail Technology (“Redtail”), a leading provider of client relationship management (CRM) solutions for financial services firms, published the results from a survey entitled “Gen Tech” which show that the majority (55%) of Gen-X and Baby Boomer financial advisory employees today use mobile devices and tablets to access their CRMs, at a percentage just ahead of Millennial employees. The survey polled more than 2,300 financial advisory professionals ranging from Millennials to Generation-Xers to Baby Boomers across the country in an effort to understand trends about technology usage across generations.
“It’s no secret that our industry is evolving at lightning speed,” said Redtail Technology CEO Brian McLaughlin. “That said, as members of the advisor fintech community, it’s Redtail’s responsibility to keep our fingers on the pulse of which technologies advisory firms are either keen or slow to adopt. We set out to uncover the technology disconnect across generations, however the results really surprised us.” Specifically, these findings reveal…
2018-11-29 15:31:50+00:00 Read the full story.

Harvard Caselaw Access Project data by the numbers – Towards Data Science

On 31 October 2018, the Library Innovation Lab at the Harvard Law School Library made nearly 6.5M US court cases available for free online. This provides an unprecedented opportunity for start-ups, established firms and researchers in legal tech to apply machine learning methods to a large corpus of legal documents.
This post provides case summary statistics on all the cases included in the dataset. The statistics are based on a manual bulk download of all jurisdictions as of 15 November 2018. As confirmed by the Harvard Caselaw team, some bulk files do not include the latest 2018 data and will be updated very soon — I will update this post in time.
There are a total of 6,454,632 cases available, split across 61 jurisdictions, including the ‘United States’ (i.e. federal cases) and some jurisdictions with no data (‘ Native American’ and‘ Regional’).
2018-12-02 17:23:46.374000+00:00 Read the full story.

Reinforcement learning: the naturalist, the hedonist and the disciplined –  A history of ideas that shaped reinforcement learning

The pursuit of artificial intelligence has always been intermingled with another struggle, more philosophical, more romantic, less tangible. The understanding of human intelligence. Although current breakthroughs in supervised learning seem to be based on optimized hardware, sophisticated training algorithms and over-complicated neural network architectures, reinforcement learning is still as old school as it gets. The idea is quite simple: you are a learning agent in an environment. If we make the general assumption that you have the goal of satisfying yourself (don’t we all?), then you perform actions. Based on these actions the environment responds with rewards and you, based on the rewards, adjust your behavior in order to maximize your satisfaction.
It did not take us long to draw a connection between the ability of living organisms to learn through reinforcement and artificial intelligence. As early as 1948, Turing described a pleasure-pain system that follows the current rules of reinforcement learning, established decades later. The first attempts of the community targeted the game of backgammon due to its simplicity, offering a small number of discrete states and simple rules. Nowadays we have AI agents that use reinforcement learning to play Atari games, Minecraft and flip pancakes. So, how did we accomplish all this? The short answer is deep learning.
This article will venture into a longer answer. It will explore the origins of the ideas behind the reinforcement learning algorithms that we have been using for decades. Our recent successes are not just a product of deep neural networks, but a deep history of observations, conclusions and attempts to comprehend the mechanisms of learning.
2018-12-01 17:09:47.694000+00:00 Read the full story.

How robotics is shaping and shifting the next generation of data centre

We are at the beginning of a long and exciting journey, where robotics can complement humans to improve data centre operations, says Giuseppe Leto, Global Data Centre Portfolio Manager at Siemens. For everyone involved in data centres, the evolution of robotics is of great interest. It is a topic that is getting relevant visibility in all kinds of industrial sectors and with that will affect data centres as well. It is well known that humans are still responsible for most of the errors in data centres: robots can help to reduce those and make centres more efficient.
2018-11-30 00:00:00 Read the full story.

Listen to the whole quant album – Cuemacro

It’s 50 years since the Beatles released the White Album. To celebrate it has been reissued with new mixes of the original tracks. I have to admit, it’s rarer these days to listen to an album the whole way through. Instead, I invariably listen to playlists on iTunes which select tracks from all manner of different artists and albums and mix them together. iTunes and Spotify have revolutionized how we listen to music, making it so find a track by nearly any artist you can think of. Whilst, I appreciate the convenience of listening to music in this way, we can sometimes miss the point.
An album, such as the White Album, is designed to take you on a journey, around a certain theme. In a sense, by only sampling a track here or there, we are basically changing that story. It’s like picking up a novel, but randomly reading chapters from it. We evidently lose something in the process. Whilst albums might be pockmarked by filler tracks, which would never make it as a single, there are often also somewhat less commercial tracks, which you’d never discover, as they’d rarely be one of the playlist tracks on iTunes or on a radio station.
I often think that people try to delve into the quant trading and investment in the same way. At first, it can be easy to be seduced by the “top selling singles” the themes which would invariably make it to an iTunes playlist (yes that top selling single called “AI and machine learning”). However, this I don’t think is the best way of doing it. Instead we need to think about quant as a whole, and listen to whole album, to see what it can offer. Here’s my checklist of how to move your investment process towards a more quant based approach.
2018-12-01 00:00:00 Read the full story.

Peak Irony: Interpersonal Skills In The Age of AI Are More Vital Than Ever

Contrary to popular belief, interpersonal skills in the age of AI are very important. Our most human qualities are what are most vital in the AI revolution.
We have published many posts here at Smart Data Collective over the past decade. Many of our posts are meant to highlight the extraordinary impact that big data is having on our world. However, some of my posts need to help people become more realistic understanding of the big data landscape. This is especially true in the arena of artificial intelligence, and the need for interpersonal skills in the age of AI. Many people have overly exaggerated beliefs about the potential of AI, or artificial intelligence. Their perceptions are especially optimistic when it comes to their predictions about big data in the workplace. Many people speculate that artificial intelligence will make many employees obsolete. They believe that even 90% of professional employees will be replaced by robots within the next 20 years. New reports have set some of these myths to rest. Joe McKendrick, a technology writer and evangelist with Forbes recently published a news article about the growing importance of interpersonal skills in an age dominated by machine learning and artificial intelligence. He points out that artificial intelligence is forcing brands to reassess their products and solutions. They are under more pressure than effort to deploy higher quality products more quickly, because advances in machine learning have heightened to consumer expectations of these services.
2018-12-02 21:18:21+00:00 Read the full story.

Global DataSphere to Hit 175 Zettabytes by 2025, IDC Says

The data we have now is huge. But size, it turns out, is a relative thing. And according to the IDC, the sum of the world’s data – the DataSphere — will grow from 33 zettabytes in 2018 to a mind-boggling 175ZB by 2025.
That robust 61% compound annual growth rate for data came out of IDC‘s latest “Data Age 2025” whitepaper, where it laid out its latest research into the phenomenal growth of data and the impact of digitization on the world around us. The 175ZB figure is 9% higher than the 2025 forecast the IDC released last year. The new report, which was sponsored by hard drive maker Seagate and can be accessed here, contained several eye-opening findings about the projected state of big data in 2025…
2018-11-27 00:00:00 Read the full story.

Machine Learning, Biased Models, and Finding the Truth

Machine learning and statistics are playing a pivotal role in finding the truth in human rights cases around the world – and serving as a voice for victims, Patrick Ball, director of Research for the Human Rights Data Analysis Group, told the audience at Open Source Summit Europe. Ball began his keynote, “Digital Echoes: Understanding Mass Violence with Data and Statistics,” with background on his career, which started in 1991 in El Salvador, building databases. While working with truth commissions from El Salvador to South Africa to East Timor, with international criminal tribunals as well as local groups searching for lost family members, he said, “one of the things that we work with every single time is trying to figure out what the truth means.”
In the course of the work, “we’re always facing people who apologize for mass violence. They tell us grotesque lies that they use to attempt to excuse this violence. They deny that it happened. They blame the victims. This is common, of course, in our world today.” Human rights campaigns “speak with the moral voice of the victims,’’ he said. Therefore, it is critical that statistics, including machine learning, are accurate, Ball said. He gave three examples of when statistics and machine learning proved to be useful, and where they failed…
2018-11-27 15:06:56+00:00 Read the full story.

Best Execution and the ‘Electronification’ of High Touch

Best execution is an evolving process, and so it is incumbent upon us to anticipate the trend in service expectations.
The unbundling of research brought about by the Markets in Financial Instruments Directive (MiFID II) put the cost of execution into the limelight with many buy-side traders responding by increasing their use of low cost, “low touch” algorithms (algos). Best execution policy disclosures also brought quantitative performance and qualitative service factors into focus. As best execution regulation and service expectations evolve, so too do the traditional “low touch” and “high touch” roles, with the road ahead leading to an electronification of “high touch” client service partnerships.
Liquidity issues in certain names, and on certain days, are always a source of frustration for buy-side traders. Markets are dynamic and liquidity in small- and mid- cap names can be here today, gone tomorrow, and a few illiquid names in a “basket” or “program” can destroy overall performance numbers. A sales trader’s ability to combine expert advice on liquidity-seeking algo parameter settings with a breadth of counterparty relationships gained through experience makes all the difference when aiming to achieve the highest possible ranking in a client’s execution performance scorecard. Buy-side traders are increasingly taking a multi-factor approach when evaluating brokers against their peers, with value-added service and other qualitative measures weighted highly next to execution quality. A “high touch” approach, in an otherwise “low touch” algo business, not only helps achieve a superior weighted average performance result, but also helps move up in the performance scorecard ranks.
2018-11-26 20:35:38+00:00 Read the full story.

To Sniff Out Insider Trading, Follow the Options Market

(Bloomberg Opinion) — Insider trading involves illegal profiting from nonpublic information by people buying and selling shares. It typically increases whenever companies are busy acquiring each other. The 42 percent rise in the Russell 3000 index during the 24 months through September included the most mergers and acquisitions in 12 years. Now the technology that created artificial intelligence is getting good at detecting stock market fluctuations that can only be explained as abnormal, sophisticated and nefarious.
Bloomberg algorithms give market participants help identifying unusual activity in stock, bond, currency and derivatives trading. The automated analysis of derivatives like options can also expose otherwise opaque insider trading activity that was once evident only with the fluctuations of the underlying assets of bonds, commodities, currencies and equities.
For example, the Bloomberg algorithm provides fresh insight into suspiciously opportunistic trading around a takeover deal last summer. During the month preceding Hartford Financial Services Group Inc.’s Aug. 22 agreement to buy Navigators Group Inc. for $70 a share, there is no public record that such a deal was in the works. But people anticipating the $2.1 billion acquisition prepared to triple their money on Aug. 14 by betting that Hartford would plummet on the news a week later. We know there was a likelihood of insider trading because the volume of Hartford options rose more than four times the 20-day average for that period eight days before the announcement, according to data compiled by Bloomberg.
2018-11-28 21:30:55+00:00 Read the full story.

Seed AI: History, Philosophy and State of the Art – Towards Data Science

“Let an ultraintelligent machine be defined as a machine that can far surpass all the intellectual activities of any man however clever. Since the design of machines is one of these intellectual activities, an ultraintelligent machine could design even better machines; there would then unquestionably be an ‘intelligence explosion,’ and the intelligence of man would be left far behind. Thus the first ultraintelligent machine is the last invention that man need ever make, provided that the machine is docile enough to tell us how to keep it under control.” (Irving John Good, 1965)
The above quote contains the first mention and explanation of the term intelligence explosion. Since then, a lot has happened in the field of Artificial Intelligence, both philosophically and practically. Although the field has made an enormous progress since 1965, the term Seed AI, closely related to the idea of the intelligence explosion, is relatively unheard of.
2018-12-02 19:22:32.147000+00:00 Read the full story.

Multitask learning: teach your AI more to make it better

Hi everyone! Today I want to tell you about the topic in machine learning that is, on one hand, very research oriented and supposed to bring machine learning algorithms to more human-like reasoning and, on the other hand, is very well known to us from basics of machine learning, but very rarely interpreted as I want to show it today. It’s called multitask learning and it has (almost) nothing to do with multitasking as on the picture above. In this post, I will show what is multitask learning for humans and algorithms, how researchers today already apply this concept, how you can use it for any problem of yours to increase your models’ performance. And, the most important, I’ll provide you with source code and explanations of 4 use cases (emotion recognition from the photos, movement identification with accelerometer data, siamese network boosting and solving Numerai challenge: all using multitask learning!), that you can use as a template (and, I hope, inspiration) for your own projects!
2018-11-30 07:01:02.216000+00:00 Read the full story.

Technology Will Determine Buy-Side Winners – Dirk Manelski, Pimco

…Manelski said his priorities include cloud migration, digital transformation and “data, data, data.” He added: “While these are the big change agenda items, priorities always need to include investing in our portfolio risk and analytics platforms.”
In another use of alternative data, Citywire reported that BlackRock’s systematic active equity team will be using a range of sources, including earning calls, internet traffic and satellite images, for a new fund range. “Artificial intelligence has graduated from a buzzword to an enabler that offers differentiated capabilities across the investment value chain,” added Deloitte.
2018-11-29 17:23:02+00:00 Read the full story.

Big Tech Primary Adopter of AI So Far; Adoption by Non-Tech Picking Up

Earlier this year, a shockwave landed in the artificial intelligence space with the news that Udi Manber, who used to run search engineering at Google, was joining the health insurer, Anthem to head up its AI group. After all, it’s not every day that industries with reputations for spurning tech and collecting reams of paper land a preeminent technologist in a burgeoning field.
But the move by Anthem could be a sign of the shift in how companies and industries think about artificial intelligence and whether it is relevant to their businesses. While in the past, AI may have been considered the province of large tech companies, increasingly non-tech firms are embracing how it can improve their performance.
In this article, we look at the artificial intelligence market and analyze which industries are investing in AI and what that means exactly in terms of useful applications that are being implemented today, and what this means for the AI talent market.
2018-11-29 21:30:37+00:00 Read the full story.

How BlueData Acquisition Will Push HPE into Containers

Hewlett-Packard Enterprise has acquired a key connective technology for how enterprises deploy artificial intelligence and big data analytics in their applications. The company announced an agreement Nov. 27 to purchase BlueData, which will help expand HPE’s product lineup in the fast-growing AI and analytics software markets. Terms of the transaction were not made available; it is expected to close within HPE’s first fiscal quarter, which ends Jan. 31.
BlueData, which offers a new-gen big-data-as-a-service (BDaaS) software platform, recently made available a new environment for AI and machine-learning developers to try out new ideas and have fun testing them. This is a new turnkey package that enables accelerated deployment of artificial intelligence, machine learning and deep learning applications in the enterprise. The BlueData AI/ML Accelerator, introduced last May, includes the software and professional services to deploy containerized multi-node sandbox environments for exploratory use cases with TensorFlow and other ML/DL tools, the 6-year-old Santa Clara, Calif.-based company said.
2018-11-28 00:00:00 Read the full story.

5 Wealth Management Tasks an Algorithm Can Do

There is a lot of excitement surrounding robotic process automation and artificial intelligence and their potential for automation in business.
A new white paper by Fiserv, a global provider of financial services technology solutions, looks at how these emerging technologies will be able to drive significant increases in productivity and efficiency in the wealth management industry. Fiserv defines robotic process automation (RPA) as the programming of machines to mimic the way humans manage and perform tasks. As a form of intelligent automation, RPA relies on software rather than actual robots to imitate the activity of humans and carry out repetitive tasks. Through the use of intelligent algorithms, RPA can react to events and triggers to follow step-by-step procedures in various scenarios. In wealth management, RPA can take over tedious and mundane tasks that humans perform, to service both the advisor and the middle and home office.
2018-11-30 00:00:00 Read the full story.

Qualcomm Launches AI Investment Fund

Wireless chip leader Qualcomm Inc. announced a $100 million investment fund focused on AI startups developing chip and connectivity technologies for emerging applications like machine learning platforms and autonomous vehicles.
As it shifts from wireless chip sets for mobile phones to a range of emerging AI applications, San Diego-based Qualcomm’s (NASDAQ: QCOM) investment arm is identifying startups that can accelerate the transition of its low-power processor technology to the network edge. The initiative dovetails with the wireless chip vendors 5G networking efforts that seek to connect devices while adding local processing. Those capabilities can then be linked to “edge clouds,” the chip maker said Wednesday (Nov. 28).
2018-11-28 00:00:00 Read the full story.

2019 AI Predictions From Forrester: Data Quality a Top Challenge

It’s difficult to make predictions, especially about the future, but we can be certain that “AI Washing” will continue to rise and flourish in 2019. That’s what market research firm Forrester calls the hype and hooplah around Artificial Intelligence (AI), the latest set of technologies that is promising to “change the world.”
In its 2019 predictions, Forrester tries to temper the “irrational exuberance for AI adoption” with a dose of reality, looking forward by observing how companies automate their work today while experimenting with adding intelligence—artificial and human—to analyzing data and making decisions. Here’s my summary of two Forrester reports published today, “Predictions 2019: Artificial Intelligence” and “Predictions 2019: Automation.”
2018-11-26 20:30:53+00:00 Read the full story.

AWS Bolsters Machine Learning Services at Re:Invent

Amazon Web Services today unveiled a slew of new machine learning services, including a version of Sagemaker that uses reinforcement learning, a specially designed inference chip, and a marketplace of machine learning algorithms.
Andy Jassy, the CEO of AWS, took the stage for a marathon keynote address at the AWS Re:Invent conference in Las Vegas, Nevada, this morning to show off the cloud giant’s progress and unveil a slew of new products and services.
2018-11-28 00:00:00 Read the full story.

AWS Launches Time-Series Database

AWS threw its hat into the nascent ring for time-series databases yesterday with the launch of AWS TimeStream, a managed time-series database that AWS says can handle trillions of events per day. Time-series databases have emerged as a best-in-class approach for storing and analyzing huge amounts of data generated by users and IoT devices. While relational and NoSQL databases are sometimes used for time-stamped and time-series data – such as clickstream data from Web and mobile devices, log data from IT gear, and data generated by industrial machinery — today’s massive data volumes from the IoT have outstripped the capability of those databases to keep up.
2018-11-29 00:00:00 Read the full story.

How AWS Continues to Re:Invent the Cloud

In the cloud industry, one thing has been fairly certain in recent years and that is the fact that Amazon Web Services will continue to announce new features and capabilities for its cloud platform at a rapid pace.
The busiest event by far for AWS is its annual AWS re:Invent conference, which is often the place where an overwhelming number of new features and services are announced—and the 2018 conference was no exception. The 2018 event ran Nov. 26-30 in Las Vegas, spanning six hotels.
At the 2017 event, AWS had a strong focus on machine learning technologies, which carried through to the 2018 edition of re:Invent. AWS, however, is not a one-trick pony and was firing on all cylinders this year, announcing new container, developer, private cloud, networking, storage and developer features across its ever expanding, vast portfolio of capabilities.
2018-11-30 00:00:00 Read the full story.

Automated Credit Trading on the Rise

…CP+ is MarketAxess’ proprietary algorithmic pricing engine for corporate bonds. The engine is updated every 15 to 60 seconds, depending on the liquidity of the instrument, and generates nearly 20 million levels per day covering 90-95% of trading activity in its markets. Coltman explained that CP+ uses machine learning to provide an indication of where a bond might trade next. “We are at the beginning of the process of learning how artificial intelligence can be incorporated into pricing engines and even pre-trade,” he added. “For example, AI could indicate the likelihood of a large block being executed, and this data could be fed up to the portfolio managers.”…
2018-11-27 17:40:12+00:00 Read the full story.

HPE Hones Its Edge Computing Offerings

At Hewlett Packard Enterprise’s Discover show in June, President and CEO Antonio Neri announced the company would invest $4 billion over four years to develop technologies and services aimed at the increasingly competitive edge computing space, saying that “the edge is everywhere technology gets put into action and I believe the edge is the next big opportunity for all of us.”
The investment bolstered what has been an aggressive rollout by HPE over the past couple of years of products and services designed for the edge, an initiative that is continuing this week at the company’s Discover 2018 Madrid show in Spain. HPE officials are announcing new Edgeline converged edge solutions that include new systems, management offerings and a platform to automatically mesh IT edge applications and operational technologies (OT) to drive autonomous decision making at the edge.
2018-11-27 00:00:00 Read the full story.

GE Healthcare Unveils New Applications Built on its Edison Intelligence Platform

GE Healthcare has announced new applications and smart devices built on Edison – a platform that helps accelerate the development and adoption of Artificial Intelligence (AI) technology and empower providers to deliver faster, more precise care.
Edison is part of GE Healthcare’s $1 billion and growing Digital portfolio and will serve as a “digital thread” for its existing AI partnerships and products. Clinical partners will use Edison to develop algorithms, and technology partners will work with GE Healthcare to bring the latest advancements in data processing to Edison applications and smart devices.
The healthcare AI market will reach $6.6 billion in 2021, and 39 percent of healthcare provider executives say that they’re investing in AI, machine learning and predictive analytics. Appreciating the technology’s potential, GE Healthcare announced the new Edison applications and Edison-powered devices at the recent 104th annual meeting of the Radiological Society of North America (RSNA).
2018-11-26 20:20:10+00:00 Read the full story.

Artificial Intelligence Has A Probability Problem

AWS just announced Amazon SageMaker Ground Truth to help companies create training data sets for machine learning. This is a powerful new service for folks who have access to lots of data that hasn’t been consistently annotated. In the past, humans would have to label a massive corpus of images or frames within video to train a computer vision model. Ground Truth uses machine learning in addition to humans to automatically label a training data set.
This is one example of an emerging theme over the past year or so — machine learning for machine learning. Machine-learning data catalogs (MLDCs), probabilistic or fuzzy matching, automated training data annotation, and synthetic data creation all use machine learning to produce or prepare data for subsequent machine learning downstream, often solving problems with data scarcity or dispersion. This is all well and good until we consider that machine learning in and of itself relies on inductive reasoning and is therefore probability-based.
2018-11-29 11:43:47-05:00 Read the full story.

TigerGraph Launches Cloud Database on AWS

A year after emerging from stealth mode, graph database startup TigerGraph is expanding its offerings to include a cloud database service that supports AI and machine learning applications. The startup announced at Amazon Web Services’ (NASDAQ: AMZN) annual gathering this week that it would offer its AI-based graph analytics platform as a cloud service, beginning with AWS and shortly adding other public cloud carriers. The cloud-based graph database service is being promoted as a faster and easier way to securely run SQL-like queries in the cloud without the need to configure or manage servers.
2018-11-27 00:00:00 Read the full story.

My talk on Machine Learning in Finance: why Alternative Risk Premia (ARP) products failed

I would like to share and introduce my talk presented at the conference on applications of machine learning for quantitative strategies (the video of my talk available here).
In my talk, I address the limitations of applying machine learning (ML) methods for quantitative trading given limited sample sizes of financial data. I illustrate the concept of probably approximately correct (PAC) learning that serves as a foundation to the complexity analysis of machine learning.
2018-11-27 19:42:16+00:00 Read the full story.

Retail Banking Lagging in Process Automation, Despite Benefits

Automation may not be the most sexy part of banking’s ongoing digital makeover. Analyzing and automating countless business processes from mobile apps to call centers can seem like a tedious affair. Even though bots and software make this easier than many think, most banks and credit unions have not done much with them due to misconceptions and overblown concerns. And you would be wrong.
A survey by Cognizant of executives from across the financial industry — retail banking, credit cards, wealth management and mortgage lending — revealed that the automation efforts at nearly two-thirds (65%) of institutions remain at the early or proof-of-concept stages… if they’ve done anything at all. And yet nine in ten respondents professed that process automation is critical to their business plans, both now and in the future.
2018-11-28 05:03:51+00:00 Read the full story.

IBM’s 8-bit AI training technique is up to 4 times faster while retaining accuracy

Computational efficiency is the name of the game in artificial intelligence (AI). It’s not easy maintaining a balance between training speed, accuracy, and energy consumption, but recent hardware advances have made the goal more attainable than it once was. Case in point: IBM will this week showcase AI training methods that result in orders of magnitude better performance than the previous state of the art. The first of the Armonk, New York company’s breakthroughs is an accelerated digital technique that achieves full accuracy with 8-bit precision. The second is an 8-bit precision technique for an analog chip — the highest of its kind to date, IBM claims — that roughly doubles accuracy.
2018-12-02 00:00:00 Read the full story.

GFT teams up with Blue Prism to push Robotic Process Automation (RPA) in the financial services industry

Collaboration between the global IT company GFT and Blue Prism, a leader in Robotic Process Automation (RPA), aims to accelerate innovation. This initiative will enable banks and insurance companies to transform business operations whilst increasing customer satisfaction
GFT Technologies SE (GFT), a global IT company focussing on the financial services industry, and Blue Prism, a leader in Robotic Process Automation (RPA), are joining forces to help banks and insurance companies increase productivity, improve customer experiences and deliver new services through automation. Both companies are committed to helping clients drive digital transformation by creating a scalable and secure digital workforce that collaborates in real-time with human colleagues.
2018-11-28 00:00:00 Read the full story.

Zero Knowledge Analytics from Dispatch (Infographic)

For every business, data in the form of consumer information, financial transactions, research, human behavior and now artificial intelligence has become crucial. It provides more insight and improves engagement with consumers and increased interactions for more predictable and consistent conversions to sales. Data is also a commodity being generated by billions of people worldwide who contribute to very few “data brokers” monetizing from the data. However, users have become increasingly dismayed and sceptic of how their data is being used.
2018-12-02 18:28:34+00:00 Read the full story.

The digital transformation puzzle: no more shying away from technology

…The ability to wield and control real-time data is truly the platform that makes DT possible. Without it, banks looking to digitally transform will lack proper insight and data analysis—making the required DT steps, such as powering advanced machine learning techniques, significantly more difficult…
2018-11-30 00:00:00 Read the full story.

5 ways disruptive technologies will transform AML compliance

There is no doubt that the financial world is undergoing radical transformation, aided by the plethora of newer and more disruptive technologies. In fact, it’s safe to say that financial services will be virtually unrecognizable in the next 10 years. However, some things remain the same. Banks will always have client, regulatory, operational and data challenges that need to be solved.
In terms of anti-money laundering (AML) and Know-Your-Customer (KYC) compliance, Artificial Intelligence (AI), in particular, has the ability to completely transform how banks perform these processes efficiently and effectively. AI is particularly valuable when performing repetitive tasks, saving valuable time, effort and resources that can be refocused on higher client-value tasks. Here are five key ways in which AI can help improve AML/KYC and client onboarding processes…
2018-12-03 00:00:00 Read the full story.

Python Vs Javascript Battle For Supremacy – codeburst

The Software industry has always been the front-runner in adopting change and innovation. Programming languages keep upgrading and new languages pops every then and now to cover up the new needs of the technology world. These poses a great challenge for the aspiring developers and also to the seasonal developers. So be the change and bring the change
The developer needs to up-skill themselves frequently to keep up with the pace. There are multiple language & framework choices for new age programmers to choose from. The fresh engineers who are graduating every year are always facing this constant question of what programming language to pick as the Career option in the field of software development.
I have coined one article which covers Top 3 languages to look for in 2018–19. Hope it helps to some extent to answer your query.
2018-11-28 11:18:56.884000+00:00 Read the full story.

3 Lessons for Investors from Microsoft’s Impressive Turnaround

As quite a few publications have been eager to mention, Microsoft’s (MSFT – Get Report) market cap has surpassed Apple (AAPL – Get Report) , making it the world’s most valuable publicly-traded company.
There’s no guarantee that Microsoft will be worth more than Apple — or for that matter, (AMZN – Get Report) or Alphabet (GOOGL – Get Report) — a year from now. But even if Microsoft only briefly holds onto the title it just obtained, obtaining it represents one more remarkable milestone for a company that not long ago had been dismissed by many as a “legacy” or “irrelevant” tech giant.
2018-12-02 10:00:00-05:00 Read the full story.

Automatic Data Processing : ADP Announces Top Workplace Trends for 2019 and Provides a Look-Back at the U.S. Labor Market in 2018

…Democratization of Data – Employers and employees will benefit from better access to deeper workplace insights.
Providing business leaders with data that delivers meaningful and actionable workforce insights, as well improving industry competitiveness, will become the new norm. An increased focus will be placed on how easy it is for leaders to consume and put data into action. HR systems will increasingly tap into artificial intelligence (AI) and machine learning (ML) to serve up insights in real time…
2019-12-03 00:00:00 Read the full story.

China’s VCs show new interest in backing foreign-bred startups

China’s venture capitalists are changing their game. Where it’s previously been difficult for foreign startups to get Chinese VC funding, Chinese VCs are beginning to look at foreign-born startups with significant interest. Shanghai-based Wei Zhou, founder and CEO of XNode, has a unique vantage point of China’s embrace of foreign innovation: XNode is a high-profile startup and corporate accelerator with a flagship space in central Shanghai’s Jing’An district, which provides open innovation training for multinationals such as Intel and Philips and a landing pad in this thriving metropolis of 25 million people for Korean and Australian startups that have received support from their respective governments.
“China’s venture capital landscape is undergoing a shuffle,” he told me, “which means that startups are scrutinized more rigorously than in the past and fewer domestic candidates pass the screening. This opens the door like never before to founders of foreign-bred startups with deep tech solutions that are serious about commercializing in the Chinese market.”
2018-12-01 00:00:00 Read the full story.

The New Banking Format: The segment of one

Arthur Clarke once observed that cave dwellers froze to death on beds of coal.This is the opening statement of Carla O’Dell and C. Jackson Grayson’s book “If Only We Knew What We Know.” At the time, their focus was the internal transfer of knowledge, particularly around best practices. However, the quote is just as applicable today when considering the topic of data itself.
In the past, FIs used their core application data for customer segmentation and analysis of product utilization, and subsequently to develop marketing programs. A decade or more ago, many branch systems were designed around these marketing initiatives. The bank representative was coached to promote whatever popped up on the screen while working with a customer. Frankly, many of those promotions, while perhaps offering something of value to the customer, were really focused on the bank’s internal desire to sell more of specific products. And it was often a “one size fits all” philosophy.
Today, the challenge is much greater as banks compete more intensely for customers. Customers are expecting their banks to engage them at their “point of life” with relevant offerings and advice. How does an FI offer something that has primary value and relevance to each unique customer – a person now known in the financial industry as “the segment of one”?
2018-11-29 10:16:48 Read the full story.

DeepMind claims early progress in AI-based predictive protein modelling

Google -owned AI specialist, DeepMind, has claimed a “significant milestone” in being able to demonstrate the usefulness of artificial intelligence to help with the complex task of predicting 3D structures of proteins based solely on their genetic sequence.
Understanding protein structures is important in disease diagnosis and treatment, and could improve scientists’ understanding of the human body — as well as potentially helping to support protein design and bioengineering.
Writing in a blog post about the project to use AI to predict how proteins fold — now two years in — it writes: “The 3D models of proteins that AlphaFold [DeepMind’s AI] generates are far more accurate than any that have come before — making significant progress on one of the core challenges in biology.”
2018-12-03 00:00:00 Read the full story.

Why IT Ops Has Become Such a Rich Target for Big Data Analytics

In the business world, we measure what we hope to impact. This leads companies to devise all sorts of metrics from a wide assortment of data. Increasingly, one of the most impactful pieces of data we’re collecting — and thus hoping to impact through big data analytics — involves IT operations itself.
It’s difficult to fathom how extraordinarily big the IT sector has become. According to Gartner, nearly $3.7 trillion will be spent this year on information technology products and services, a 4.5% increase from 2017. Three thousand, seven hundred billion dollars is quite a bit of money, and in fact is equivalent to about 4.5% of all the money spent on planet Earth in 2018 (the new Mars rover notwithstanding).
By all accounts, we’re getting quite a bit of value out of this massive investment in technology. One only has to look at one’s smart phone and the array of Internet-connected services that it uses to get an inkling of the impact of technology. When one considers all servers, storage arrays, and networking gear powering applications in massive data centers sprinkled around the world, the extent of digitization boggles the mind.
2018-11-27 00:00:00 Read the full story.

How to Avoid the Potential Dangers of AI, Robots and Big Tech Companies

If you plan to live another 10 years, you should expect to live in a world with machines doing things you don’t like doing today. Shooting for another 20? Even more will be done without your lifting the proverbial finger. It’s not only menial tasks such as cleaning, laundry and dishes. High-end services previously not accessible to you will now be in your economic grasp. Your personal robot will know you better than you know yourself. This almost unimaginable lifestyle could become routine for the masses, given the tangible achievements of artificial intelligence (AI) and robotics to date and the low-latency-coupled-with-high-bandwidth-connectivity that 5G is on track to provide.
2018-11-29 21:00:31+00:00 Read the full story.

The Development of AI Ethics Must Keep Pace with Innovation

The ability of artificial intelligence to make ethically sound decisions is a hot topic in debates around the world. The issue is particularly prevalent in discussions on the future of autonomous cars, but it spans to include ethical conundrums similar to those depicted in sci-fi flicks like Blade Runner.
These high-level debates are mainly about a future that’s still years away, but it is true that AI is already becoming part of our lives. Think Siri, Amazon’s Alexa, and the photo-sorting function on many smartphones. The popularity of technologies like these already influences how people think about machine intelligence. In a recent survey, 61 percent of respondents said society would become “better” or “much better” from increased automation and AI. But getting to truly humanlike intelligence will take time. And the science must go hand-in-hand with thinking about the implications of creating intelligent machines — possibly to the level of robot rights and the nature of consciousness. As AI innovation continues to move forward at warp speed, it will be important for the development of ethics surrounding the technology to keep up.
2018-11-26 20:00:07+00:00 Read the full story.

Optimized Prime: How AI Powers Amazon’s 1-Hour Deliveries

By the time someone clicks “buy” on Amazon, Jenny Freshwater’s team has probably expected it. Freshwater is a software director in Amazon’s Supply Chain Optimization Technologies group. Her team forecasts demand for everything sold by Amazon worldwide. This task, into which NPR got exclusive insight, underlies the entire Amazon retail operation. And it’s central to Amazon’s wooing of some 100 million people who shell out up to $119 a year for a Prime subscription, which guarantees two-day shipping.
Inside Amazon, corporate executives like to evoke magic when they talk about fast delivery. For months, they used the code name Houdini before launching their fastest service, Prime Now, which delivers household basics within hours. But a lot of it is thanks to artificial intelligence. With AI, computers analyze reams of data, making decisions and performing tasks that typically require human intelligence. AI is key to Amazon’s retail forecasting on steroids and its push to shave off minutes and seconds in the rush to prepare, pack and deliver.
2018-11-29 21:20:04+00:00 Read the full story.

Anxious Actuaries are 97.2892% Certain They Need New Game Plan

Riley Howsden has one of those hip jobs everyone loves to hate. He works at L.A.-based video game producer Riot Games, where business attire includes hoodies, the food comes free and his job entails deducing what kinds of products gamers might buy. Aggressive players, for instance, might splurge on samurai-assassin gear for their characters.
Howsden’s current data science gig couldn’t be further afield from his previous one as an insurance actuary — a job so stereotypically mundane the inside joke is “an extroverted actuary is someone who looks at other people’s shoes.” “If I was to write a cover letter for a job, it’s much easier for me to detail my passion for video games than it is to detail my passion for insurance,” said Howsden, 32, who spent weekends playing video games in his hometown of Oshkosh, Nebraska, population 884.
Howsden’s career pivot is at the root of some soul searching going on within actuarial communities. Seasoned actuaries often earn more than $200,000, and the field perennially ranks near the top of “best career” surveys. A survey from this summer called actuarial science the most valuable bachelor’s degree based on pay and employment. However, industry conventions are rife with warnings that data scientists are encroaching on actuaries’ turf, and that their lack of speaking skills keeps them locked in bookish roles at insurers.
2018-11-30 00:00:00 Read the full story.

Never start with a hypothesis – Towards Data Science

Lies, damned lies, and STAT101
Setting up Frequentist statistical inference is a ballroom dance; its steps are action-action-worlds-worlds. There’s a nice foxtrot rhythm to it. Unfortunately, most people bungle it by starting on the wrong foot. Here’s how to dance it right.
2018-11-30 23:30:02.562000+00:00 Read the full story.

MIT Ethics Reading Group Explores the Ethical Dimensions of AI

For years, the tech industry followed a move-fast-and-break-things approach, and few people seemed to mind as a wave of astonishing new tools for communicating and navigating the world appeared on the market. Now, amid rising concerns about the spread of fake news, the misuse of personal data, and the potential for machine-learning algorithms to discriminate at scale, people are taking stock of what the industry broke. Into this moment of reckoning come three MIT students, Irene Chen, Leilani Gilpin, and Harini Suresh, who are the founders of the new MIT AI Ethics Reading Group.
All three are graduate students in the Department of Electrical Engineering and Computer Science (EECS) who had done stints in Silicon Valley, where they saw firsthand how technology developed with good intentions could go horribly wrong. “AI is so cool,” said Chen during a chat in Lobby 7 on a recent morning. “It’s so powerful. But sometimes it scares me.” The founders had debated the promise and perils of AI in class and among friends, but their push to reach a wider audience came in September, at a Google-sponsored fairness in machine learning workshop in Cambridge. There, an MIT professor floated the idea of an ethics forum and put the three women in touch.
2018-11-29 21:10:21+00:00 Read the full story.

Is it the best equation for healthcare?

Over the past few years, data had served as one of the rich sources of insight for every industry. If analyzed correctly, one can get their decision-making juices flowing. In fact, the time has come when several organizations have reached the tipping point where they require making use of such technology which has the ability to consume and analyze that information. The only problem is that they have built capacity for analytics production, but not insight.
Other than data analytics, big data has been generating lots and lots of hype in almost every industry and healthcare is no exception. If you ask me to sum up the entire healthcare industry in a few words I would simply say it has been a continuous evolution, progression, discovery, and invention. Of courses, problems have always remained a constant factor but there have been solutions too. For example, antibiotics and keyhole surgery to artificial hearts and 3D imaging studies, innovation is given to providers across all disciplines. But do you know what’s more bothering the healthcare industry these days?
In the present scenario, healthcare seems to be transforming into a consumer-driven industry where providers aren’t found competing against themselves but also creating the most attractive, intuitive, and impactful experiences for a population that is no longer shy to express what it wants. User-friendly experience is something that everyone craves for. Apart from this, gone are the days when intuition was enough for quality patient outcomes. And data science seems to be providing pretty much rewarding outcomes in fast, scalable, and precise ways. Read away!
2018-12-03 12:16:33+00:00 Read the full story.

An international lawyer sets out his vision of AI regulation

In recent years there has been a deluge of articles describing, often in apocalyptic terms, the imminent and fundamental changes to the social and political fabric artificial intelligence will bring. Our roads will soon be brimming with autonomous or semiautonomous cars, weaving in seamless formation according to the whims of an algorithm digesting data in real time; repetitive tasks will no longer be the preserve of blood pumping homosapiens, but intelligent robots fed with code, bits and bytes, potentially leading to economic displacement on a dramatic scale.
Although a concern of technologists for many years, in 2018 AI regulation had its moment in the spotlight. An international consensus has emerged: some form of regulation is required to deal with the revolutionary and widespread changes resulting from AI.
2018-11-29 00:00:00 Read the full story.

Salesforce is not the fastest-growing enterprise software company ever — it’s Amazon

AWS is about twice the size of Salesforce and is growing faster. Both companies had a big week, with Salesforce reporting better-than-expected results and AWS garnering excitement at its re:Invent conference. In 2016, Salesforce committed to spending $400 million over four years on AWS.
Marc Benioff reiterated a familiar refrain this week after Salesforce’s earnings report, telling CNBC’s Jim Cramer that his company “remains the fastest-growing enterprise software company of all time.” But he’s wrong. Amazon Web Services, with $6.7 billion in quarterly revenue, is about twice the size of Salesforce, which on Tuesday reported fiscal third-quarter sales of $3.4 billion. AWS was created inside Amazon in 2006, about seven years after Benioff co-founded Salesforce.
Benioff isn’t completely off the mark. Among independent software companies, Salesforce did reach $10 billion in annual sales faster than any of its predecessors. If it can meet Benioff’s prediction of reaching $20 billion by 2022, it will be the quickest to that mark as well. But AWS could surpass $70 billion that year, according to a Nov. 28 report from Jefferies.
2018-12-01 00:00:00 Read the full story.

TD Ameritrade Institutional to Hold Its First Advisory-Focused Fintech Competition

Three finalists have been chosen for TD Ameritrade Institutional’s first-ever competition for technology geared toward financial advisors and their clients.
Emotomy, a cloud-based wealth management platform, submitted a risk assessment tool that will use machine-learning algorithms to warn registered investment advisors when client accounts are at risk of leaving to prompt more timely interaction with clients. Derrick Wesley, the owner of iMar Learning Solutions in Van Alstyne, Texas, proposed a financial literacy application that teaches the importance of financial planning to anyone from aged 5 up through retirement. The third, Dynamic Wealth Solutions, a Southfield, Mich.-based registered investment advisory, seeks to use natural language processing, a form of artificial intelligence, to develop a voice-activated assistant.
2018-11-27 11:22:46-05:00 Read the full story.

5 Tools That Use Big Data For Social Media Optimization

There are several cutting-edge tools available that use big data for social media optimization, and there is much to be learned from them. Big data is changing the nature of digital marketing in ways we never envisioned 20 years ago. This is most evident with SEO, but is also true with social media, and using big data for social media. A number of social media optimization toolsuse big data to help marketers expand their reach.
Social media is still one of the most affordable online marketing strategies. Contrary to paid advertising, your business can benefit from increased visibility and brand awareness without paying for it directly. If you want an effective social media marketing campaign, you’re going to need to invest time, effort, and energy into the right tools to help you manage and optimize your posts.
The following social media tools will help you plan posts efficiently, measure and analyze the results of marketing efforts, find relevant and interesting content to share, and use big data analytics to improve your campaigns. They all rely extensively on big data to get the best results.
2018-12-02 21:03:56+00:00 Read the full story.

Famous paintings could be 3D printed to be hung around the world

rt galleries could one day replace priceless masterpieces with replicas that look virtually identical to the real thing thanks to a new 3D printing technique.
The technique combines artificial intelligence (AI) with 3D printing to faithfully recreate colours from an original artwork with remarkable accuracy.
It works by using 3D printing to stack ten different transparent inks in wafer-thin thin layers on a canvas. The method, known as RePaint is combined with a decades-old technique called half-toning, where an image is created by tiny coloured dots rather than in continuous tones. Blending both of these captures the nuances of the different colours in extraordinary detail, making a copy indistinguishable from the original.
Scientists at a the Massachusetts Institute of Technology (MIT) who created the system claim it is four times more accurate than current printing techniques.
2018-11-29 00:00:00 Read the full story.

Amazon Prime Health is coming, according to an early investor

John Doerr, an early investor in both Google and Amazon, believes Jeff Bezos’s company will offer a “Prime Health” for medical and health products, Christina Farr reports at Doerr remains close with the Amazon founder, and didn’t mind speculating on where Bezos might take his company next. “Imagine what it’s going to be like when he rolls out Prime Health, which I’m convinced he will,” he said, speaking at a Forbes healthcare conference in San Francisco Thursday. Doerr didn’t say whether he’d talked to Bezos directly about health products.
Given the billions Doerr has made making bets on companies’ futures, his opinion matters. Doerr joined the famed venture capital firm Kleiner Perkins in 1980 and is now its chairman. Part of the reason Amazon might excel in health is data. The company already has a mountain of data on people and their shopping and buying habits, and that could be used to help serve their health needs, Doerr suggested. Amazon Prime now has more than 100 million users.
2018-11-30 06:15:19 Read the full story.

Your next colleague could be a robot. Here’s how to get along

For most people today, robots and smart systems are servants that work in the background, vacuuming carpets or turning lights on and off. Or they’re machines that have taken over repetitive human jobs from assembly-line workers and bank tellers. But the technologies are getting good enough that machines will be able work alongside people as teammates – much as human-dog teams handle tasks like hunting and bomb detection.
There are already some early examples of robots and people teaming up. For example, soldiers use drones for surveillance and ground robots for bomb disposal as they carry out military missions. But the U.S. Army envisions increased teaming of soldiers, robots, and autonomous systems in the next decade. Beyond the military, these human-robot teams will soon start working in fields as diverse as health care, agriculture, transportation, manufacturing, and space exploration.
Researchers and companies are exploring lots of avenues for improving how robots and artificial intelligence systems work – and technical advances are important. As an applied cognitive scientist who has conducted research on human teaming in highly technical settings, I can say human-robot systems won’t be as good as they could be if the designers don’t understand how to engineer technologies that work most effectively with real people. A few basic concepts from the deep body of scholarly research into human teamwork can help develop and manage these new relationships.
2018-11-29 07:00:40 Read the full story.

How digital technology is reshaping CX and operations in the utility sector

…One of the areas of transformation, according to the report, involves applying digital technologies such as big data analytics, IoT, or cloud computing to hard, expensive physical infrastructure assets needed to generate, transmit and sell the utility’s service. The report explains, “Predictive analytics can save significantly on maintenance costs, sensor technology in IoT can provide a real-time view of performance of the network, while drone technology might improve monitoring or response in a hostile environment. “When coupled with emerging technologies like artificial intelligence and machine learning, it might be able to improve response times or even stop problems before they occur.”
2018-11-27 15:08:44+11:00 Read the full story.

Brain Corp will supply Walmart with AI services

Autonomous machines aren’t of much use without a management layer to orchestrate them. San Diego startup Brain Corp offers one such solution, dubbed BrainOS, which combines off-the-shelf hardware, sensors, and software to provide “brains” for industrial and commercial robots. And business is growing at a steady clip, evidenced last year by a $114 million funding round led by SoftBank’s Vision Fund and Qualcomm Ventures and a partnership with floor cleaning company Tennant.
Today, in another sign of Brain Corp’s expansive ambitions, the eight-year-old company announced that it has entered a relationship with Walmart to supply the retailer’s stores with artificial intelligence (AI) services.
2018-12-03 00:00:00 Read the full story.

How to Gather Your Own Data by Conducting a Great Survey

In this post, we’ll learn to create an online survey and how to prevent some common mistakes made in surveys. We’ll cover all steps of the survey process, including:

  • Selecting a population
  • Sampling methods
  • Making a data analysis plan
  • Writing good questions
  • Distribution options

Data Scientists know that even the slickest code, the best data analysis, and the most beautiful visualizations are worthless if the data it’s based on is unsound. So how can we ensure that the data from our survey is accurate and meaningful?  It’s a process, but let’s start with a quick test. Can you spot the problems with these real survey questions?
2018-11-27 15:01:00+00:00 Read the full story.

MI6 spymaster cautions Russia but eyes China’s growing power

…Younger said he had been struck by President Xi Jinping’s “made in China” ambitions and that Beijing could within decades dominate all of the key emerging technologies, particularly artificial intelligence, synthetic biology and genetics…
2018-12-03 13:15:07+00:00 Read the full story.

Korean Telecom Plans Use of Big Data to Prevent Diseases in Ghana

South Korea’s telecommunications company, KT, has signed an agreement on the use of Big Data in preventing infectious diseases with the Ghana Health Service.
Attendees at the signing ceremony in Accra, Ghana, included Yoon Jong-Jin, KT’s senior executive vice president in charge of public relations and Dr Anthony Nsiah Asare, director-general of Ghana Health Service (GHS)
2018-11-26 20:10:33+00:00 Read the full story.

The 1996 law that made the web is in the crosshairs

…“Section 230 means they [tech companies] are not required to fact-check or scrub every video, post, or tweet,” Wyden said. “But there have been far too many alarming examples of algorithms driving vile, hateful, or conspiratorial content to the top of the sites millions of people click onto every day –companies seeming to aid in the spread of this content as a direct function of their business models.”
And the harm may get a lot worse. Future bad actors may use machine learning, natural language, and computer vision technology to create convincing video or audio footage depicting a person doing or saying something provocative that they didn’t really do or say. Such “Deepfake” content, skillfully created and deployed with the right subject matter at the right time, could cause serious harm to individuals, or even calamitous damage to whole nations. Imagine a deep-faked president taking to Twitter to declare war on North Korea…
2018-11-29 09:00:42 Read the full story.

Britain’s data commissioner launches investigation into UK use of facial recognition

The information watchdog has launched a formal investigation into the police use of facial recognition technology following trials across the country, The Daily Telegraph has learned.
Information commissioner Elizabeth Denham opened the inquiry after expressing “significant concern” over the legality and effectiveness of the technology, which was used at last year’s Notting Hill Carnival and Remembrance Sunday. Earlier this year, Ms Denham threatened to launch legal action against the Home Office and the National Police Chiefs’ Council over concerns relating to the intrusive nature of the technology, which uses artificial intelligence to identify potential troublemakers by scanning thousands of faces recorded by surveillance cameras. In a blog post earlier this year, Ms Denham said: “How facial recognition technology is used in public spaces can be particularly intrusive. It’s a real step change in the way law-abiding people are monitored as they go about their daily lives.
2018-12-03 00:00:00 Read the full story.

Autonomous vehicles will completely change how we shop

Since 2015, there have been at least 57 major retail bankruptcies. And after more than 5,000 store closures in 2017, we are on track to surpass that number in 2018. A combination of increased online shopping, declining foot traffic in malls, and competition from digital native brands has made retail a more challenging industry. Today’s retailers will have to adapt quickly to succeed in a rapidly changing market dominated by e-commerce.
Brick and mortar stores are being pummeled by e-commerce retailers with drastically lower overheads. Companies like Warby Parker and Casper have successfully connected the dots between the online and physical retail experience. But for the next pioneers of commerce, there is something even more disruptive happening around the corner: autonomous delivery.
2018-12-02 00:00:00 Read the full story.

Rolls-Royce demonstrates fully autonomous passenger ferry in Finland

Rolls-Royce has publicly demonstrated what it calls the “world’s first fully autonomous ferry” on a trip between Parainen and Nauvo in Finland. The British company entered a research partnership with Finnish state-owned Finferries back in May, revealing plans to “optimize ship safety and efficiency” through developing and demonstrating autonomous ferry technologies.
The first fruits of this project — which is called SVAN (Safer Vessel with Autonomous Navigation) — have been revealed today after the team conducted around 400 hours of trials in the Turku archipelago using an adapted 53.8-meter Falco car ferry. “Today’s demonstration proves that the autonomous ship is not just a concept, but something that will transform shipping as we know it,” said Rolls-Royce’s president for commercial marine Mikael Makinen, in a press release.
2018-12-03 00:00:00 Read the full story.

Behind a Paywall/Registration Wall

DeepMind’s autonomy thrown into doubt as study reveals almost a fifth of staff came from Google

Google looks increasingly at risk of breaking its pledge to keep DeepMind independent from the rest of the business, as a Telegraph investigation finds almost a fifth of the artificial intelligence arm’s staff have been moved in from other Google divisions.
London-based artificial intelligence company DeepMind is one of Britain’s most successful technology firms of the last decade. It owns an AI-powered app called Stream that uses NHS patient data to help doctors diagnose and detect illnesses. However, Google’s increasing involvement with the firm has raised questions over the privacy of p…
2018-12-01 00:00:00 Read the full story.

Here’s how to use Duplex, Google’s crazy new service that impersonates a human voice to make appointments on your behalf

ixel users one step closer to never having to make another phone call again thanks to a new voice-enabled tool that can schedule appointments and book dinner reservations for you.
Duplex, the newest artificial intelligence tool for Google Assistant, has started rolling out to Pixel phone owners, a Google spokesperson confirmed to Business Insider. The technology can perform actual phone calls for you in order to make you a haircut appointment, or book a restaurant reservation.
Google debuted the technology back in May at its annual I/O conference, but never provided a clear timeline of when Duplex would be availab…
2018-12-02 00:00:00 Read the full story.

Darktrace chief executive: Cyber security is a global arms race – and we plan to win

Even by today’s standards, it was an audacious heist. Last year, hackers in Finland used a large decorative fish tank located inside a US casino to crack into its computer system and target high-rollers.
The stakes were high. Their aim was to use the fish tank, which was connected to the internet via the casino’s internal network, to find its database of big-spending gamblers and pinch their details as they continued to count their chips. If it …
2018-12-03 00:00:00 Read the full story.

China’s ‘unethical’ experiment to create gene-edited babies could spell disaster for humanity

apel in Westminster Abbey, Stuart Russell gave a lecture on technological threats to humanity.
Mr Russell, 56, is a world-renowned British computer scientist, and the threat he was talking about was Artificial Intelligence. The title of his lecture was “Have the Machines Taken Over?”
But at one point, to illustrate how controls to potentially catastrophic advances can be put in place, he veered off-topic, onto the field of human gene editing.
“Most technologists will tell you it’s impossible to stop research, it’s impossible to stop progress,” he said, in the crisp, clear phrasing with which he habitually explain…
2018-12-02 00:00:00 Read the full story.

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