AI & Machine Learning News. 20, July 2020

The Artificial Intelligence and Machine Learning Newsletter by CloudQuant

The Artificial Intelligence and Machine Learning News clippings for Quants are provided algorithmically with CloudQuant’s NLP engine which seeks out articles relevant to our community and ranks them by our proprietary interest score. After all, shouldn’t you expect to see the news generated using AI?


TSA Traveler Throughput

TSA Traveler Throughput

I thought I would make one myself this week!

I had to fiddle the ‘Date’ axis in Paint.net as cutecharts does not have very good axis control.

Data Source : https://www.tsa.gov/coronavirus/passenger-throughput
Language : Python Jupyter Labs (CloudQuant) with CuteChart Library (https://github.com/cutecharts/cutecharts.py)
Paint.net : CuteCharts is not great at axis control so had to add the dates manually.
Font : xkcd http://www.xkcd.com/fonts/xkcd-Regular.otf

On 6/26 GAP (Nasdaq:GPS) and Kanye West announced a partnership

Another neat post by redditor pdwp90, one of the QuiverQuant guys.

2020-06-27 Read the full story…

CloudQuant Thoughts : Needless to say, the Robinhood traders went crazy and so did the GPS stock. Its range for the day was $2.63 from an opening price of $12.53. Its end of day volume was 135.5m shares vs the previous day of 10.1m.

UK Uber drivers are taking its algorithm to court – TechCrunch

A group of UK Uber drivers has launched a legal challenge against the company’s subsidiary in the Netherlands. The complaints relate to access to personal data and algorithmic accountability.

Uber drivers and Uber Eats couriers are being invited to join the challenge which targets Uber’s use of profiling and data-fuelled algorithms to manage gig workers in Europe. Platform workers involved in the case are also seeking to exercise a broader suite of data access rights baked into EU data protection law.

It looks like a fascinating test of how far existing legal protections wrap around automated decisions at a time when regional lawmakers are busy drawing up a risk-based framework for regulating applications of artificial intelligence.

2020-07-20 00:00:00 Read the full story…
Weighted Interest Score: 2.3991, Raw Interest Score: 1.0465,
Positive Sentiment: 0.1086, Negative Sentiment 0.3653

CloudQuant Thoughts : A very interesting case and article that wraps up AI, Open Algorithms and Personal Data Ownership!!

These 3 Stocks Have a Killer Advantage

The technology hardware business has a reputation for being very cyclical and economically sensitive. However, the following three stocks — Taiwan Semiconductor Manufacturing (NYSE:TSM), ASML Holdings (NASDAQ:ASML), and NVIDIA (NASDAQ:NVDA) — have all absolutely trounced the market this year, despite their hardware-focused businesses and a pandemic-fueled recession.

What’s their secret? Each of these companies plays in some of the best long-term growth markets of 5G and artificial intelligence (AI) computing. While the COVID-19 pandemic is clearly weighing on demand for certain tech products, the 5G and AI races are proving to be sectors in which everyone is still competing, and demand for these leading-edge products isn’t slowing down.
2020-07-20 00:00:00 Read the full story…
Weighted Interest Score: 2.2672, Raw Interest Score: 1.1072,
Positive Sentiment: 0.4861, Negative Sentiment 0.1485

CloudQuant Thoughts : Taiwan Semiconductor have also just announced that they will halt all new Huawei orders after US tightens restrictions.

Stock Analysis in Python

It’s easy to get carried away with the wealth of data and free open-source tools available for data science. After spending a little bit of time with the quandl financial library and the prophet modeling library, I decided to try some simple stock data exploration. Several days and 1000 lines of Python later, I ended up with a complete stock analysis and prediction tool. Although I am not confident (or foolish) enough to use it to invest in individual stocks, I learned a ton of Python in the process and in the spirit of open-source, want to share my results and code so others can benefit.

This article will show how to use Stocker, a Python class-based tool for stock analysis and prediction (the name was originally arbitrary, but I decided after the fact it nicely stands for “stock explorer”). I had tried several times to conquer classes, the foundation of object-oriented programming in Python, but as with most programming topics, they never quite made sense to me when I read the books. It was only when I was deep in a project faced with a problem I had not solved before that the concept finally clicked, showing once again that experience beats theoretical explanations! In addition to an exploration of Stocker, we will touch on some important topics including the basics of a Python class and additive models. For anyone wanting to use Stocker, the complete code can be found on GitHub along with documentation for usage. Stocker was designed to be easy to use (even for those new to Python), and I encourage anyone reading to try it out. Now, let’s take a look at the analysis capabilities of Stocker!

2020-07-13 00:00:00 Read the full story…
Weighted Interest Score: 3.8655, Raw Interest Score: 2.1849,
Positive Sentiment: 0.5042, Negative Sentiment 0.0000

CloudQuant Thoughts : An interesting article, but it would be much easier to just sign up to CloudQuant and use a Python based app that is used by professional traders for live trading as well as backtesting!


Transform 2020 digital conference

See all the presentations here

Female leaders talk ethics, representation, and more at Transform 2020’s Women in AI breakfast

At Transform’s second annual Women in AI Breakfast presented by Capital One and Intel, powerful women from tech companies across the industry gathered to talk about how women, particularly women of color, can take their seats at the table in the technology industry.

“Much has been written about the industry’s pipeline problem, and how we can increase diversity in tech companies,” said Carla Saavedra Kochalski, director of conversational AI and messaging products at Capital One who provided opening remarks. “Many say they don’t hire women or people of color candidates because there aren’t enough qualified candidates. And it’s true — there are fewer of women than men with computer science degrees.”

2020-07-17 00:00:00 Read the full story…
Weighted Interest Score: 3.1932, Raw Interest Score: 1.1959,
Positive Sentiment: 0.1443, Negative Sentiment 0.1649

Uber: Tooling is a critical part of AI development and deployment

Uber employs thousands of machine learning models to inform all aspects of its business, according to chief scientist Zoubin Ghahramani. He revealed this tidbit during a session at VentureBeat’s Transform 2020 summit, during which he spoke about Uber’s use of AI and internet of things (IoT) technologies at the edge and in datacenters around the world.

Contrary to popular belief, autonomous vehicles aren’t the top driver of AI and machine learning at Uber, according to Ghahramani. (Uber’s Advanced Technologies Group has been developing and testing self-driving cars for passenger pickup since 2015.) Rather, the bulk of the company’s algorithms are designed to handle natural language interactions across Uber’s mobile apps and to detect fraud and other issues. In May, for example, Uber rolled out an AI system to verify drivers are wearing masks in accordance with the company’s pandemic health and safety policies.

2020-07-17 00:00:00 Read the full story…
Weighted Interest Score: 3.9115, Raw Interest Score: 1.6062,
Positive Sentiment: 0.1606, Negative Sentiment 0.0964

eBay CTO: AI is now an ‘ecosystem’ for us

While eBay could have used any number of existing AI platforms to enhance its various products, the company instead elected to build its own AI system — dubbed Krylov — in-house and make it open source for anyone to use. That decision appears to be paying off.

The San Jose-based company has made no secret of its AI ambitions over the past four years, hoovering up technical talent via a…
2020-07-17 00:00:00 Read the full story…
Weighted Interest Score: 3.5040, Raw Interest Score: 1.4541,
Positive Sentiment: 0.1818, Negative Sentiment 0.2121

Silicon Valley execs and Pentagon AI chief talk AI at the edge

When considering transformational ways to use computer vision on the edge in devices like robots, drones, cameras, and other devices, Booz Allen Hamilton VP Josh Sullivan advises caution, urging people to take security seriously on what’s become a whole new attack vector.

“For me, deploying an AI model in your IT environment is an entirely new attack vector. I’ve seen a model working c…
2020-07-17 00:00:00 Read the full story…
Weighted Interest Score: 3.4663, Raw Interest Score: 1.5513,
Positive Sentiment: 0.2266, Negative Sentiment 0.2440

Alexa and Google Assistant execs on future trends for AI assistants

Businesses and developers making conversational AI experiences should start with the understanding that you’re going to have to use unsupervised learning to scale, said Prem Natarajan, Amazon head of product and VP of Alexa AI and NLP. He spoke with Barak Turovsky, Google AI director of product for the NLU team, at VentureBeat’s Transform 2020 AI conference today as part of a conversation about future trends for AI assistants.

Natarajan called unsupervised learning for language models an important trend for AI assistants and an essential part of creating conversational AI that works for everyone. “Don’t wait for the unsupervised learning realization to come to you yet again. Start from the understanding that you’re going to have to use unsupervised learning at some level of scale,” he said.
2020-07-16 00:00:00 Read the full story…
Weighted Interest Score: 3.3629, Raw Interest Score: 1.7057,
Positive Sentiment: 0.1365, Negative Sentiment 0.0910

How BMW and Malong used edge AI and machine learning to streamline warehouse and checkout systems

During a panel today at VentureBeat’s Transform 2020 conference, speakers including BMW Group’s Jimmy Nassif, Red Hat’s Jered Floyd, and Malong CEO Matt Scott discussed the challenges and opportunities in AI with respect to edge computing and IoT. While each came from a different perspective — Nassif from robotics, Floyd from retail — all three were in agreement that AI has the potential to accelerate existing work while enabling entirely new capabilities.

BMW produces a car every 56 seconds, Nassif says. Millions of parts flow into the automaker’s factories from over 4,500 suppliers involving 203,000 unique parts numbers, which translates to about 100 end-customer options. (99% of orders are completely unique.) As BMW’s car sales doubled over the past decade to 2.5 million in 2019, this created a logistics dilemma — one that was solved in part by Nvidia’s Isaac, Jetson AGX Xavier, and DGX platforms. Nassif says BMW is tapping them to develop five navigation and manipulation robots that transport materials around warehouses and organize individual parts.
2020-07-17 00:00:00 Read the full story…
Weighted Interest Score: 2.5573, Raw Interest Score: 1.0150,
Positive Sentiment: 0.3310, Negative Sentiment 0.2648

Intel VP: AI-aided defect detection is a killer app for industrial IoT

Computer vision has become one of AI’s most promising applications, combining ever-improving cameras with faster and smarter automated object recognition. During today’s Transform 2020 digital conference, Intel VP Brian McCarson spoke with VentureBeat CEO Matt Marshall about computer vision’s role in the growing industrial internet of things (IIoT) market. The conversation highlighted a particularly compelling emergent use case: hugely improved product defect detection that promises to improve the reliability of everything from computer screens to cars.

Manufacturers seeking to eliminate product defects haven’t historically lacked staff or defect screening expertise, McCarson said — they have been held back by limitations of the human eye. In modern consumer products, defects can be microscopic or near-microscopic, such as bad screen pixels or surface issues in aluminum car transmission components. While people are great at detecting motion and changes in patterns, they can’t always spot tiny details like these, so as computer vision evolved, Intel saw an opportunity.
2020-07-17 00:00:00 Read the full story…
Weighted Interest Score: 2.1864, Raw Interest Score: 1.2367,
Positive Sentiment: 0.3975, Negative Sentiment 0.3092


Deutsche’s Corporate Bank Launches First Client-Facing Bot

Deutsche Bank announced the onboarding of a new digital employee, named Blue Bot ‘Yi’ within its Corporate Bank division in China.

Undertaking a client-facing role, the new employee is responsible for handling financial reports including real-time customized transaction reports and cash pooling reports, and for processing direct client enquiries, which has already been successfully done for two of Deutsche Bank’s clients in China.

2020-07-20 09:47:10+00:00 Read the full story…
Weighted Interest Score: 4.7368, Raw Interest Score: 2.6327,
Positive Sentiment: 0.4827, Negative Sentiment 0.0439

Goldman and Mastercard invest in Bond

Goldman Sachs and Mastercard have joined a $32 million Series A funding round for Bond, a US startup connecting digital brands to banking partners.

Coatue led the round, with participation from Canaan, B Capital, XYZ Ventures and angels including former Morgan Stanley CEO John Mack.

Bond is building a fintech platform designed to act as a growth engine for “digital brands” that want to provide access to capital to their customers, and banking…
2020-07-20 00:01:00 Read the full story…
Weighted Interest Score: 4.5659, Raw Interest Score: 2.5487,
Positive Sentiment: 0.2999, Negative Sentiment 0.0000

Want To Learn Keras? Here Are 8 Free Resources

A deep learning library in Python, Keras is an API designed to minimise the number of user actions required for common use cases. It is one of the most used deep learning frameworks among developers and finds a way to popularity because of its ease to run new experiments, is fast and empowers to explore a lot of ideas. Built on top of TensorFlow 2.0, Keras is an industry-strength framework that can scale to large clusters of GPUs or an entire TPU…
2020-07-19 12:30:12+00:00 Read the full story…
Weighted Interest Score: 4.3779, Raw Interest Score: 2.7708,
Positive Sentiment: 0.2729, Negative Sentiment 0.0630

Kepler AutoML Targets Next-Gen Business Analysts

As more companies roll out digital infrastructure, they are ingesting greater volumes of data that can be used by business analysts to gauge customer intent and boost transactions. Complexity and lack of data scientists have made that transition harder for mid-size firms looking to monetize “dark” data.

Machine learning vendors are therefore automating key aspects of data science workflows that would allow domain experts to customize pipelines and algorithms based on specific data types. AutoML approaches are promoted as boosting the quantity and quality of machine learning models produced on, say, a monthly basis.
2020-07-15 00:00:00 Read the full story…
Weighted Interest Score: 4.0369, Raw Interest Score: 2.1429,
Positive Sentiment: 0.1323, Negative Sentiment 0.1323

Greater Acceptance of AI Has Resulted in Lower Satisfaction Levels

The COVID-19 crisis has accelerated the use of digital technologies and has increased the application of artificial intelligence (AI) into all aspects of the consumer experience. As the pandemic continues to impact the way consumers interact with businesses and with each other, the demand for contactless or non-touch interfaces increases. This has forced organizations to find new ways to integrate advanced intelligence into the entire customer journey.

2020-07-20 00:05:24+00:00 Read the full story…
Weighted Interest Score: 3.8926, Raw Interest Score: 1.3756,
Positive Sentiment: 0.4486, Negative Sentiment 0.1346

Webinar: The bottom line – simplifying digital evolution in financial services

90% of IT leaders in financial services believe their firm will need to invest in digital projects just to survive the rapidly changing market. Today, digitalisation is not only an important strategic development, it’s a fight for survival.

While this provides a new direction for the industry, the reality is that IT leaders and developers in financial services face myriad challenges in the post-digital world – from outdated infrastructure that n…
2020-07-17 14:37:12+00:00 Read the full story…
Weighted Interest Score: 3.6323, Raw Interest Score: 1.5579,
Positive Sentiment: 0.2967, Negative Sentiment 0.2967

Databases vs. Hadoop vs. Cloud Storage

How can an organization thrive in the 2020s, a changing and confusing time with significant Data Management demands and platform options such as data warehouses, Hadoop, and the cloud? Trying to save money by bandaging and using the same old Data Architecture ends up pushing data uphill, making it harder to use. Rethinking data usage, storage, and computation is a necessary step to get data back under control and in the best technical environments to move business and data strategies forward.

William McKnight, President of the Data Strategy firm the McKnight Consulting Group, offered his advice about the best data platforms and architectures in his presentation, Databases vs. Hadoop vs. Cloud Storage at the DATAVERSITY® Enterprise Analytics Online Conference. McKnight explained that today’s Data Management needs call for leveling up to technology better suited to obtaining all data fast and effectively.
2020-07-15 07:35:56+00:00 Read the full story…
Weighted Interest Score: 3.3355, Raw Interest Score: 1.9197,
Positive Sentiment: 0.3200, Negative Sentiment 0.0640

On Whether AI Can Form ‘Intent’ Including In The Case Of Autonomous Cars

Is AI more akin to humans and therefore able to form intent, or is AI more similar to a toaster and unable to have any substance of intent? Lest you think this is an entirely abstract point and not worthy of real-world attention, consider the legal ramifications of whether AI can form intent and whether this is noteworthy or not.

In our approach to jurisprudence, we give a tremendous amount of importance to intent, sometimes referred to as scienter in legal circles, and criminal law makes use of intent to ascertain the nature of the crime that can be assigned and the penalty that might ride with the crime undertaken. A toaster that goes awry will hopefully be a mildly adverse consequence (I can choose to eat the burnt toast or toss it into the trash), while if an AI system that can drive a car goes awry, the result can be catastrophic.

2020-07-16 12:30:17+00:00 Read the full story…
Weighted Interest Score: 3.3202, Raw Interest Score: 1.2711,
Positive Sentiment: 0.0871, Negative Sentiment 0.2460

Crazy Idea No. 46: Making Big Data Beneficial for All

Now here’s a crazy idea: What if the data we all generate on a day to day basis benefited us, instead of the companies that collect it? It may sound nuts at first, but some AI experts see a future in which people hold full control over their data and smart digital assistants infused with AI work to protect and monetize a person’s individual’s data for his or her benefit.

This vision of a more equitable big data world is one that’s held by Sri Ambati. The H2O.ai founder and CEO sees a day not too far in the future in which people are empowered to control their own data as an asset, and even to profit directly from their data, which is something that only a handful of individuals are currently able to do.

“Today, whether we want it or not, our data is stored on giant social networks,” Ambati tells Datanami. “Our clicks are essentially stolen away and leave a fingerprint of who we are digitally. In that sense, we don’t have ownership. We’ve just kind of given carte blanche ownership to the companies with the largest Internet presence, if you will.”

2020-07-17 00:00:00 Read the full story…
Weighted Interest Score: 3.2036, Raw Interest Score: 1.3643,
Positive Sentiment: 0.3234, Negative Sentiment 0.0606

Best Research Papers From ACL 2020

ACL is the leading conference in the field of natural language processing (NLP), covering a broad spectrum of research areas in computational linguistics. Due to the COVID-19 risks, ACL 2020 took place 100% virtually, similar to other big academic conferences of this year.

However, as always, it was the best place to learn about the latest NLP research trends and cutting-edge research papers in language modeling, conversational AI, machine translation, and other NLP research topics.

Following the long-standing tradition, the best paper awards were announced during the last day of the main conference. In this article, we’ve summarized the key research ideas of the papers that received the Best Paper Award and Honorable Mentions at ACL 2020.

  • Beyond Accuracy: Behavioral Testing of NLP Models with CheckList
  • Don’t Stop Pretraining: Adapt Language Models to Domains and Tasks
  • Tangled up in BLEU: Reevaluating the Evaluation of Automatic Machine Translation Evaluation Metrics

2020-07-14 15:20:50+00:00 Read the full story…
Weighted Interest Score: 3.0933, Raw Interest Score: 1.8101,
Positive Sentiment: 0.3222, Negative Sentiment 0.2605

AI will augment, not destroy humanity

I’ve spent years talking about Artificial Intelligence (AI).

Bearing in mind that we always talk about AI in the context of the Turing Test – a test that Alan Turing created back in 1950 – it’s not surprising. That test is that we will have achieved the true opportunities for technological development when a machine can fool a panel of experts that it is human. We have not passed that test yet, no matter what you’ve read, but we will. In fact, AI is developing at such a pace that it may be sooner than many expected. We are on the brink of General AI – where machines can multi-task – and I expect we will achieve Super AI – where machines are more intelligent than humans – to be achieved before 2040.

This last category of AI, Super AI, was forecast to be achieved sometime in the 2040s a while ago, and then we enter Skynet, the scary Terminator world of Cyberdine. Actually, no we don’t. That vision of the Terminator is where machines become the enemies of humans. It’s a bit like Ex Machina and other science fiction visions of the future. The scary future where humans make machines that are more intelligent than us and then the machines take over. It’s great science fiction, but it’s not realistic. It’s not realistic as, when you think about it, every movement of technological progress in our past has helped humanity, not destroyed it.
2020-07-15 06:28:08+00:00 Read the full story…
Weighted Interest Score: 3.0495, Raw Interest Score: 1.6119,
Positive Sentiment: 0.3635, Negative Sentiment 0.4583

An economic system with growing gaps and an upside-down model

The interaction of Humans and Machines is becoming more core to my focus. Finance is an integral part of our socio-economic fabric and Artificial intelligence (in a very broad sense) is shaping an increasing part of Fintech innovation.

In this post I am inspired from some of the points we raised in the great conversation in June which was part of the Thinkathon of Fintech.TV spearheaded by Dr. Jane Thomason. (Watch Future Economic System and the Role of the Democratic State) with Emily Landis-Walker, Lord (Chris) Holmes MBE, Lawrence Wintermeyer, Lore…
2020-07-14 00:00:00 Read the full story…
Weighted Interest Score: 2.7783, Raw Interest Score: 1.0636,
Positive Sentiment: 0.2704, Negative Sentiment 0.2524

DL Is Not Computationally Expensive By Accident, But By Design

Researchers from MIT recently collaborated with the University of Brasilia and Yonsei University to estimate the computational limits of deep learning (DL). They stated, “The computational needs of deep learning scale so rapidly that they will quickly become burdensome again.”

The researchers analysed 1,058 research papers from the arXiv pre-print repository and other benchmark references in order to understand how the performance of deep learning techniques depends on the computational power of several important application areas.
2020-07-20 09:30:00+00:00 Read the full story…
Weighted Interest Score: 2.7509, Raw Interest Score: 1.7834,
Positive Sentiment: 0.3082, Negative Sentiment 0.1761

To Centralize or Not to Centralize Your Data–That Is the Question

Should you strive to centralize your data, or leave it scattered about? It seems like it should be a simple question, but it’s actually a tough one to answer, particularly because it has so many ramifications for how data systems are architected, particularly with the rise of cloud data lakes.

In the old days, data was a relatively scarce commodity, and so it made sense to invest the time and money to centralize it. Companies paid millions of dollars to ensure their data warehouses were filled with the cleanest and freshest data possible, for historical reporting and analytics use cases.
2020-07-14 00:00:00 Read the full story…
Weighted Interest Score: 2.7451, Raw Interest Score: 1.5156,
Positive Sentiment: 0.2747, Negative Sentiment 0.1418

Mosaic Smart Data Launches Stand-Alone Data Normalisation

Mosaic Smart Data (Mosaic), the real-time capital markets data analytics company, is launching its data normalisation process as a new stand-alone service. Mosaic will employ its best-in-class enrichment technology and flexible data model to process firms’ transaction data, allowing institutions to analyse their activity in a given asset class at both the micro and macro levels, and in real-time, for the first time.

Mosaic Smart Data has combined its deep domain expertise in financial products, data science and software engineering to develop a service that cleanses, normalises and enriches streaming data in real-time for all major FICC asset classes including cash and derivatives. The service can be provided in the cloud or deployed on premises behind the client’s firewall. The resulting data is stored and made available via an API allowing data to be accessed remotely, making digital and distributed working feasible.
2020-07-20 09:15:19+00:00 Read the full story…
Weighted Interest Score: 2.6087, Raw Interest Score: 1.6614,
Positive Sentiment: 0.2110, Negative Sentiment 0.3428

Building an Open Cloud Data Lake Future

The explosion of data and the need for business agility to leverage that data for competitive advantage are driving a massive surge of data lake innovation. We’ve moved past first-generation on-premises Hadoop-based data lakes to focus on building next-generation data platforms in the cloud. Organizations of all sizes recognize that cloud data lakes, with separation of compute and data, give them the flexibility and freedom they need both today and tomorrow.

A key advantage of cloud data lakes is their open architecture, which minimizes the risk of vendor lock-in as well as the risk of being locked out of future industry innovation. As the cloud data lake evolves to support a wide range of production analytical and data processing use cases, it’s important to ensure that it maintains this open architecture in the future. A rich ecosystem of open source projects, technology vendors and cloud providers has emerged to make that a reality.
2020-07-20 00:00:00 Read the full story…
Weighted Interest Score: 2.5925, Raw Interest Score: 1.3843,
Positive Sentiment: 0.4027, Negative Sentiment 0.0503

With Modernization Comes Data Challenges

Modernization is driving many of today’s enterprise data strategies—and cloud stands out as the primary vehicle for attaining this modernization. However, many enterprises are struggling with data quality issues, as well as integrating cloud-based and on-premise data.

That’s the word from a recent survey of 1,840 data executives and professionals, released by Progress (“The 2020 Data Connectivity Survey Report”). The 2019 survey collected input from respondents across more than 13 distinct industries worldwide to identify patterns and insights for ongoing data management strategies.
2020-07-13 00:00:00 Read the full story…
Weighted Interest Score: 2.5817, Raw Interest Score: 1.5987,
Positive Sentiment: 0.2108, Negative Sentiment 0.2108

5 Typical Mindset Mistakes of Aspiring Data Scientists

I’ve worked with over 500 aspiring data scientists in the last few years and I’ve seen some typical mindset mistakes they tend to make. In this article, I’d like to share five of these.

2020-07-20 13:08:59.981000+00:00 Read the full story…
Weighted Interest Score: 2.5408, Raw Interest Score: 1.4256,
Positive Sentiment: 0.3075, Negative Sentiment 0.3494

Machine Learning challenges in legacy organisations

Fans of machine learning suggest it as a possible solution for everything. From customer service to finding tumours, any industry in which big data can be easily accessed, analysed and organised is ripe for bringing about new and compelling use cases. This is especially attractive for legacy organisations, such as financial services firms, looking to gain an advantage.

These businesses are usually well embedded in their markets, fighting with competitors over small margins and looking for new ways to innovate and drive efficiency. They also have an abundance of historical and contemporary data to exploit. One asset any start-up lacks is owned historical data, which gives legacy firms an edge in the competitive landscape. The promise of machine learning is therefore particularly seductive – feed in your extensive customer and business insights along with your desired outcome and let algorithms work out the best path forward.
2020-07-14 15:15:03 Read the full story…
Weighted Interest Score: 2.5202, Raw Interest Score: 1.6064,
Positive Sentiment: 0.4431, Negative Sentiment 0.2216

Standard Chartered signs quantum computing research deal with USRA

Standard Chartered has signed a quantum computing research agreement with the Universities Space Research Association (USRA). Standard Chartered has worked with USRA on quantum research since 2017

Standard Chartered’s Dr. Alexei Kondratyev, global head of data science and innovation, leads the collaboration.

Kondratyev and USRA had success in investigating the quantum annealing approach to computational problems in portfolio optimisation. The bank says that there are a number of promising use cases for quantum computing. These include machine learning and discriminative models with uses in credit scoring and generating trading signals.

2020-07-16 10:30:22+00:00 Read the full story…
Weighted Interest Score: 2.4746, Raw Interest Score: 1.5494,
Positive Sentiment: 0.3541, Negative Sentiment 0.1328

IT Industry Embraces Data-Led Approach As New Buzzword Emerges

Feel like you’re hearing the word “data-driven” more than ever? Here’s what to know about the IT industry’s latest data-led approach.

Over recent years the term ‘data-driven’ has become somewhat of a buzzword. For more than 10 years, it has been widely accepted that all businesses, including those who are already technology-centric, should embrace advancements in digital technology. It was believed that the best way to achieve this was to become increasingly data-driven. Today, an ever-expanding body of evidence suggests that the future of business, which includes those in the IT sector, is not data-driven, but rather data-led. This shift is partially due to the fact that, despite their best intentions, many businesses were battling to become as ‘data-driven’ as they believed they should be. A new buzzword has emerged and data-led decision making is impacting all corners of the IT industry.

A data-led IT firm can utilize artificial intelligence (AI) to create one-on-one conversations with its clients.
2020-07-19 23:27:32+00:00 Read the full story…
Weighted Interest Score: 2.4704, Raw Interest Score: 1.2038,
Positive Sentiment: 0.6125, Negative Sentiment 0.0845

The LIBOR transition: why financial organisations need to work smarter and not harder

For the last 50 years global banks have based their short-term interest rates on a market reference rate known as the London Interbank Offered Rate (LIBOR). It’s estimated that $350 trillion dollars in financial derivatives and other financial products are tied to LIBOR via contracts. This critical information is stored in scanned images of lengthy and unstructured documents within bank records.

Following multiple criminal settlements dating back to 2012 after the discovery of “rate fixing” known as the LIBOR scandal the rate is being withdrawn at the end of 2021. This leaves banks around the world with a problem – any contracts that persist beyond December next year that rely on the existence of LIBOR are not legal/valid. Now the challenge is to rewrite millions of contracts requiring a substantial legal spend and administrative effort to maintain regulatory compliance.

More technically savvy organisations are taking a very different approach. They are looking to the very latest advances in AI and Machine Learning to solve the problem far more quickly, efficiently and with lower costs. Cognitive Machine Reading (CMR) provides the core technology at the heart of a complete end-to end-solution constructed by partners, EvoluteIQ.
2020-07-16 23:01:20+00:00 Read the full story…
Weighted Interest Score: 2.3275, Raw Interest Score: 1.2283,
Positive Sentiment: 0.1271, Negative Sentiment 0.2541

Turn Your Data into Revenue with Azure Data Analytics

Fully unlocking the value of your data and streaming analytics on Azure to deliver meaningful insights means developing a plan for managing, optimizing, securing, and scaling data to meet the unique requirements of your business.

In this webinar, Jeremy Frye and Dan King, Navisite’s data analytics experts, will provide a roadmap to delivering Azure data analytics quickly and efficiently within your organization.

Join our webinar as we:

  • Outline the state of typical environments relative to data analytics capabilities
  • Review the underlying Azure tools and technologies that can support your strategy
  • Share a practical roadmap for leveraging Azure enhancements and advanced analytics
  • Explain how Azure Data Analytics Services can support your business

2020-07-16 00:00:00 Read the full story…
Weighted Interest Score: 2.2676, Raw Interest Score: 1.3605,
Positive Sentiment: 0.2268, Negative Sentiment 0.0000

Gartner reveals Top Supply Chain Technology Trends in 2020

AI in the supply chain consists of a toolbox of technology options that help companies understand complex content, engage in a natural dialogue with people, enhance human performance, and take over routine tasks.

“AI technology is present in a lot of already existing solutions, but its capabilities evolve on a constant basis,” Mr. Titze added. “Currently, the technology primarily helps supply chain leaders solve long-standing challenges around data silos and governance. Its capabilities allow for more visibility and integration across networks of stakeholders that were previously remote or disparate.”

2020-07-20 09:39:58+10:00 Read the full story…
Weighted Interest Score: 2.2214, Raw Interest Score: 1.4617,
Positive Sentiment: 0.1949, Negative Sentiment 0.0585

Gaussian Process Regression on Molecules in GPflow

This post demonstrates how to train a Gaussian Process (GP) to predict molecular properties using the GPflow library by creating a custom-defined Tanimoto kernel to operate on Morgan fingerprints. Please visit my GitHub repo for the Jupyter notebook!

In this example, we’ll be trying to predict the experimentally-determined electronic transition wavelengths of molecular photoswitches, a class of molecule that undergoes a reversible transformation between its E and Z isomers upon irradiation by light.
2020-07-19 22:56:52.816000+00:00 Read the full story…
Weighted Interest Score: 2.1996, Raw Interest Score: 0.9893,
Positive Sentiment: 0.1254, Negative Sentiment 0.1811

Democratizing Data: Do Your People Have the Access They Need?

Organizations have invested heavily in engineering resources to centralize data across the enterprise, often creating sophisticated environments with robust data pipelines. But even as they have successfully gathered and corralled data this way, many still struggle with effectively sharing and orchestrating the data across the enterprise.

That’s a pressing concern because, to successfully experiment, explore and activate data for the entire organization, IT, analytics and marketing teams must all have the data access they need to succeed. This notion isn’t new, but for many businesses, despite their commitment to democratizing data, that access—leveraging each group’s strengths—is insufficient or absent.
2020-07-13 00:00:00 Read the full story…
Weighted Interest Score: 2.1806, Raw Interest Score: 1.1229,
Positive Sentiment: 0.3417, Negative Sentiment 0.2116


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