AI & Machine Learning News. 16, March 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?


AlphaGo – The Movie | Full Documentary

With more board configurations than there are atoms in the universe, the ancient Chinese game of Go has long been considered a grand challenge for artificial intelligence. On March 9, 2016, the worlds of Go and artificial intelligence collided in South Korea for an extraordinary best-of-five-game competition, coined The DeepMind Challenge Match. Hundreds of millions of people around the world watched as a legendary Go master took on an unproven AI challenger for the first time in history.

Directed by Greg Kohs with an original score by Academy Award nominee, Hauschka, AlphaGo chronicles a journey from the halls of Oxford, through the backstreets of Bordeaux, past the coding terminals of Google DeepMind in London, and ultimately, to the seven-day tournament in Seoul. As the drama unfolds, more questions emerge: What can artificial intelligence reveal about a 3000-year-old game? What can it teach us about humanity?

CloudQuant Thoughts : Self Isolating? Here is a way to spend an entertaining hour and a half learning all about the AlphaGo project.

CloudQuant proves value in PA Signals alt data set

CloudQuant says it has proven the value in the Precision Alpha Machine Learning Signals (PA Signals) alternative data set. Its detailed data science study shows a long-short portfolio outperforms the equal-weight S&P 500 ETF by an average of 37.9 per cent per year after transaction costs.

CloudQuant found that over 91.5 per cent of the total return is pure alpha. The results of the study are significant to the 99th per cent level.

Cutting-edge machine learning is transforming quantitative analysis for portfolio managers and traders. PA Identifies structural breaks and exposes investment signals that market participants are currently unable to see. The PA Signal offers a favourable risk-adjusted return that can be used to create large-scale investment algorithms.
2020-03-13 00:00:00 Read the full story…
Weighted Interest Score: 12.5769, Raw Interest Score: 3.3426,
Positive Sentiment: 0.2228, Negative Sentiment 0.2228

CloudQuant Thoughts : Our news scraper did a good job this week picking up this excellent article and rating it very highly. This is what we do at CloudQuant.

AI Can Detect Coronavirus Infections Far Faster Than Humans

Using 5,000 confirmed cases as their training data, scientists at the Alibaba DAMO Academy built an algorithm they claim can detect coronavirus infections in CT scans in just 20 seconds and with 96% accuracy, according to Chinese outlet Sina Tech News.

In February, Qiboshan Hospital in Zhengzhou became the first place to use Alibaba’s AI to detect coronavirus, and an additional 100 hospitals reportedly plan to adopt the system.

Alibaba’s isn’t the only AI helping doctors to detect coronavirus in CT scans, either…

2020-03-10 00:00:00 Read the full story…

CloudQuant Thoughts : Lots of moves in the industry to assist in this global emergency including free access to GPUs for Corona AI research, demands for open access to all data and White House requests for Silicon Valley to assist in developing solutions and Google web page for citizens to figure out if they need a test and where the nearest test center was.

An implant uses machine learning to give amputees control over prosthetic hands

Researchers have been working to make mind-controlled prosthetics a reality for at least a decade. In theory, an artificial hand that amputees could control with their mind could restore their ability to carry out all sorts of daily tasks, and dramatically improve their standard of living.

However, until now scientists have faced a major barrier: they haven’t been able to access nerve signals that are strong or stable enough to send to the bionic limb. Although it’s possible to get this sort of signal using a brain-machine interface, the procedure to implant one is invasive and costly. And the nerve signals carried by the peripheral nerves that fan out from the brain and spinal cord are too small.

A new implant gets around this problem by using machine learning to amplify these signals. A study, published in Science Translational Medicine today, found that it worked for four amputees for almost a year. It gave them fine control of their prosthetic hands and let them pick up miniature play bricks, grasp items like soda cans, and play Rock, Paper, Scissors.

2020-03-04 00:00:00 Read the full story…

CloudQuant Thoughts : Non invasive nerve signal detection will not only assist many people with a disabilities but will also provide assistance for the elderly and open up the huge future (sci-fi!) market for physical augmentation.

20+ Machine Learning Datasets & Project Ideas

To Build a perfect model, you need a large amount of data. But finding the right dataset for your machine learning and data science project is sometimes quite a challenging task. There are many organizations, researchers, and individuals who’ve shared their work, and we will use their datasets to build our project.

So in this article, we are going to discuss 20+ Machine learning and Data Science dataset and project ideas that you can use for practicing and upgrading your skills.

2020-03-20 00:00:00 Read the full story…
Weighted Interest Score: 7.5461, Raw Interest Score: 2.1555,
Positive Sentiment: 0.1130, Negative Sentiment 0.0969

ING Invests In Natural Language Processing Technology

ING’s reputation for adopting cutting edge technologies was enhanced today with the announcement that it would make an investment in London-based provider of natural language processing (NLP) technology Eigen Technologies (‘Eigen’).

ING has invested in London-based #fintech @Eigen_Tech, a provider of natural language processing (#NLP) technology. Together they will work on establishing best-in-class NLP models for the #financialindustry. #innova…
2020-03-13 11:25:26+00:00 Read the full story…
Weighted Interest Score: 5.4463, Raw Interest Score: 2.5210,
Positive Sentiment: 0.4449, Negative Sentiment 0.0000

Reinforcement Learning In Finance – A Newbie In Portfolio Selection And Allocation

Ever heard about financial use cases of reinforcement learning, yes but very few. One such use case of reinforcement learning is in portfolio management. Earlier Markowitz models were used, then came the Black Litterman models but now with the advent of technology and new algorithms, reinforcement learning finds its place in the financial arena.

Portfolio selection and allocation have been a manual task majorly. Using reinforcement learning, the…
2020-03-16 11:30:00+00:00 Read the full story…
Weighted Interest Score: 5.1796, Raw Interest Score: 2.6539,
Positive Sentiment: 0.3611, Negative Sentiment 0.0181

Data Science Fails: Building AI You Can Trust

Industries from insurance and healthcare to banking and retail are aggressively working to integrate AI and machine learning models into their operations to maximize profits, reduce customer churn, operate more efficiently, and gain a significant advantage over competitors. However, before any business can make AI technology a central part of their organization’s success, they must first be able to trust the technology.

Regulate Your AI Bias : Businesses need to make sure that any AI solutions they implement are free from human biases and are built using best data science practices. Toward this end, it’s important to take care to avoid common data science mistakes, including:

  • Don’t buy into hype: Make sure your data science team is not caught up in the hype of a new algorithm that is generating lots of buzz.
  • Choose the right AI model: Avoid algorithm bias by allowing a competition between a champion and a challenger model to decide which is the better option.
  • Leave presumption at the door: Don’t assume you know which algorithm is best for your data in advance.

2020-03-09 00:00:00 Read the full story…
Weighted Interest Score: 5.1462, Raw Interest Score: 1.7707,
Positive Sentiment: 0.2724, Negative Sentiment 0.3859

Is Machine Learning or Deep Learning Best for Your AI Project?

When a business is engaged in digitization, adopting digital technologies to change a business model and provide new opportunities, the discussion inevitably rolls around to how to incorporate AI.

Software developers face decisions on which advanced analytic techniques are within reason to incorporate. Viewing members of a team assembled to work on projects incorporating AI, the data scientist is likely to have the best grasp of the risks versus the rewards of different tools and approaches, suggests a recent article in Data Science Central.

Powerful and reasonably-mature machine learning techniques are the most widely adopted. Deep learning describes deep neural networks and reinforcement learning. Deep learning encompasses convolutional neural networks (CNNS), recurrent neural networks (RNNs), long short-term memory networks (LSTMs) and generative adversarial networks (GANs). In applications, these would cover image and video processing and search, text and audio processing, game play as optimization and several versions of time series forecasting.

However, deep learning solutions typically require a larger volume of data, are difficult to train and require specialized skills to build, implement and maintain. These all heighten the risk. So whether deep learning techniques should be recommended for a company’s digital journey needs to be carefully considered.

2020-03-12 21:30:17+00:00 Read the full story…
Weighted Interest Score: 4.5245, Raw Interest Score: 2.3549,
Positive Sentiment: 0.2411, Negative Sentiment 0.1298

Top Challenges Startups Face While Implementing Artificial Intelligence

Artificial intelligence (AI) is the crown of every tech-powered business enterprise — whether small or big. And embracing new opportunities with AI is something every business must do to stay relevant in their industry. Implementing artificial intelligence in business will provide a direct impact on the success of the companies ranging from improved decision-making to better use of the extensive data generated.

However, business-friendly it may sound; the path to implementing artificial intelligence in business is not a smooth ride. While larger businesses find themselves in a better position, the same cannot be said about startups. There are some typical challenges that startups face when it comes to implementing AI in their organisation. In this article, we will discuss six such challenges to implement AI in startups vs in larger organisations.

2020-03-14 10:30:00+00:00 Read the full story…
Weighted Interest Score: 4.3458, Raw Interest Score: 2.0090,
Positive Sentiment: 0.2411, Negative Sentiment 0.4018

Syncsort Partners with Databricks to Support Cloud Initiatives

Syncsort is partnering Databricks to support cloud initiatives for critical mainframe and IBM i data, enabling enterprises to leverage Syncsort Connect products to access, transform, and deliver mainframe data to Delta Lake.

Organizations rely on Databricks to process massive amounts of data in the cloud and power AI, machine learning and business insights. Syncsort Connect features a design once, deploy anywhere architecture that provides a graphical interface to deploy mainframe to cloud data transformation pipelines.

Integration with Syncsort Connect products enables the combination of the Databricks platform with Syncsort’s unrivaled ability to integrate previously inaccessible mainframe and IBM i data for analytics and data science.

2020-03-09 00:00:00 Read the full story…
Weighted Interest Score: 4.3342, Raw Interest Score: 2.1038,
Positive Sentiment: 0.1791, Negative Sentiment 0.2686

Google’s Neural Tangents library gives ‘unprecedented’ insights into AI models’ behavior

Google today made available Neural Tangents, an open source software library written in JAX, a system for high-performance machine learning research. It’s intended to help build AI models of variable width simultaneously, which Google says could allow “unprecedented” insight into the models’ behavior and “help … open the black box” of machine learning.

As Google senior research scientist Samuel S. Schoenholz and research engineer Roman Novak explain in a blog post, one of the key insights enabling progress in AI research is that increasing the width of models results in more regular behavior and makes them easier to understand. By way of refresher, all neural network models contain neurons (mathematical functions) arranged in interconnected layers that transmit signals from input data and slowly adjust the synaptic strength (weights) of each connection. That’s how they extract features and learn to make predictions.

2020-03-13 00:00:00 Read the full story…
Weighted Interest Score: 4.2634, Raw Interest Score: 2.2261,
Positive Sentiment: 0.2087, Negative Sentiment 0.1391

Google Launches Beta Version of Cloud AI Platform Pipelines

A scalable machine learning workflow involves several steps and complex computations. These steps include data preparation and preprocessing, training and evaluating models, deploying these models and much more. While prototyping a machine learning model can be seen as a simple and easygoing task, it eventually becomes hard to track each and every process in an ad-hoc manner.

To simplify the development of machine learning models, Google launches the beta version of Cloud AI Platform Pipelines, which will help to deploy robust, repeatable machine learning pipelines along with monitoring, auditing, version tracking, and reproducibility. It ensures to deliver an enterprise-ready, easy to install, a secure execution environment for the machine learning workflows.

2020-03-14 12:30:00+00:00 Read the full story…
Weighted Interest Score: 4.1419, Raw Interest Score: 2.0460,
Positive Sentiment: 0.3197, Negative Sentiment 0.0000

ML Engineer, Data Scientist, Research Scientist: What’s the Difference?

If you have to write an artificial intelligence (AI) or machine learning (ML) job description, it can be difficult to convey precisely what kind of new employee you want to hire. Doing so requires using the right language, plus understanding what type of role is most appropriate for what you want to achieve.

To guide you through the challenging process of recruiting top AI talent, we’ll start by looking at the differences between different AI & ML roles. Then, we’ll discuss who should be your first hires depending on the approach you choose for your ML projects. We also recommend you make sure that you don’t do these seven things to scare off the AI talent you’re trying to hire.

THE DIFFERENCES BETWEEN POPULAR AI & ML JOBS
Someone who’s unfamiliar with the various job titles associated with AI & ML may quickly get overwhelmed by the perceived lack of distinction between them. This breakdown should help.

2020-03-10 16:35:47+00:00 Read the full story…
Weighted Interest Score: 4.0761, Raw Interest Score: 2.0392,
Positive Sentiment: 0.2017, Negative Sentiment 0.1008

Tips For Data Scientists & Data Engineers To Work Together

Noticeably, the demand for big data professionals has been higher than ever, where data scientists, data engineers and machine learning engineers are being ranked among the top emerging jobs of the industry. We agree that data is king; however, many companies are struggling to integrate a proper data science team into their engineering workflows. This is because of lack of knowledge and an improper understanding of the field.

Ever since big data and analytics became one of the lucrative career paths for the youth, there has been an ongoing discussion about the differences between various data-related roles — especially data scientists and data engineering. And for people to get into this field or for organisations to build a strong team to handle their data, it is imperative to understand the field properly.

One needs to understand that a data science degree isn’t suitable for a data engineering role. While data science deals heavily with mathematics, data engineers, in contrast, primarily deal with the tech side of data — building data pipelines. However, both roles have ‘big data’ common in them.

2020-03-16 12:30:00+00:00 Read the full story…
Weighted Interest Score: 3.8611, Raw Interest Score: 2.1773,
Positive Sentiment: 0.4147, Negative Sentiment 0.1944

Eigen and ING target financial industry with NLP-driven data extraction

Eigen Technologies, a U.K. startup that offers natural language processing (NLP) technology to help companies extract meaningful data from documents, has closed a $42 million series B round of funding. This includes a fresh $5 million tranche from Dutch finance giant ING, following an initial $37 million raise back in November.

Additionally, Eigen and ING announced a deeper working partnership to establish “best-in-class NLP models” that are ful…
2020-03-13 00:00:00 Read the full story…
Weighted Interest Score: 3.7037, Raw Interest Score: 1.9166,
Positive Sentiment: 0.1127, Negative Sentiment 0.1879

Data Scientist: Education, Training, Interviewing

Your typical data scientist works with various forms of data to discover insights and knowledge. Then they develop products and services that support optimal decision-making.

The data can be structured (coming from a pre-defined data model and residing in relational databases) or unstructured (having no pre-defined format, such as text files or user-generated content).

A data scientist is responsible for understanding and aggregating these diff…
2020-03-11 00:00:00 Read the full story…
Weighted Interest Score: 3.4727, Raw Interest Score: 1.9001,
Positive Sentiment: 0.2643, Negative Sentiment 0.1007

How Automation, AI, and Data Integration are Transforming the Pharmaceutical Industry

Click to learn more about author Joe Rymsza.

Pharma companies are more challenged than ever to bring drugs to market safely and cost-effectively. Roadblocks to success include the ever-evolving regulatory environment, growing patient safety concerns, and the burden of outdated technology solutions. Today many businesses find themselves burdened with rigid and costly compliance processes. Furthermore, there is a schism between organizations and t…
2020-03-16 07:35:26+00:00 Read the full story…
Weighted Interest Score: 3.4321, Raw Interest Score: 1.9044,
Positive Sentiment: 0.3304, Negative Sentiment 0.2915

Machine Learning Leading to Revolution in Clinical Data Management

“What I’ve been looking at for the past few years is how things are evolving within the clinical trial space, and what impact that’s going to have on clinical data management,” said Francis Kendall, Senior Director of Biostatistics and Programming at Cytel, explained to attendees in Orlando during the Summit for Clinical Ops Executives (SCOPE).

We’re going to see a shift in how clinical evidence is produced and where it’s produced from, said Kendall. “It’s a new paradigm about data usage,” he said “We have traditional clinical trials, and they will always remain, but we’re starting to see things like pragmatic trials or synthetically controlled models. How do we deal with that data?”

2020-03-12 21:30:19+00:00 Read the full story…
Weighted Interest Score: 3.2823, Raw Interest Score: 1.8018,
Positive Sentiment: 0.1461, Negative Sentiment 0.0974

Google Launches TensorFlow Quantum

The worlds of quantum computing and machine learning are coming together with TensorFlow Quantum (TFQ), a new library unveiled today by Google.

Google has been one of the leaders in the emerging field of quantum computing, where computers are able to manipulate multiple qubits, compared to the binary bits that regular computers can use. The Mountain View, California tech giant declared “quantum supremacy” last year as a result of its progress in the field.

But for all advancement that’s been made, it found that “there’s been a lack of research tools to discover useful quantum ML models that can process quantum data and execute on quantum computers available today,” the company says.

2020-03-09 00:00:00 Read the full story…
Weighted Interest Score: 3.2662, Raw Interest Score: 1.7462,
Positive Sentiment: 0.1722, Negative Sentiment 0.0738

How to Deliver a Data Science Project Successfully

It is demanding to know where to begin once you’ve decided that, yes, you wish to dive into the fascinating world of data and AI. Just having a look at all the technologies you need to understand all the tools you’re supposed to master is enough to make you confused.

Well, luckily for you, creating your first data project is actually not difficult as it seems. Becoming data-powered is first and most foremost about having to learn the basic steps and following them to go from raw data to create a machine learning model, and in the end to operationalization.

Let’s jump into the following steps that will help you in successfully delivering a data science project.

2020-03-08 18:21:32+00:00 Read the full story…
Weighted Interest Score: 3.0904, Raw Interest Score: 1.5218,
Positive Sentiment: 0.1806, Negative Sentiment 0.1806

Three Tricks to Amplify Small Data for Deep Learning

It’s no secret that deep learning lets data science practitioners reach new levels of accuracy with predictive models. However, one of the drawbacks of deep learning is it typically requires huge data sets (not to mention big clusters). But with a little skill, practitioners with smaller data sets can still partake of deep learning riches.

Deep learning has exploded in popularity, with good reason: Deep learning approaches, such as convolutional neural networks (used primarily for image data) and recurrent neural networks (used primarily for language and textual data) can deliver higher accuracy and precision compared to “classical” machine learning approaches, like regression algorithms, gradient-boosted trees, and support vector machines.

But that higher accuracy comes at a cost. Deep learning models are much more complex and typically require much more data to deliver better predictions. And of course, running all that data requires more computer horsepower, typically in the form of GPU-equipped clusters. It’s no wonder that the world’s leaning practitioners of deep learning are companies with names like Google, Facebook, and Microsoft, which have a ton of data and compute capacity on which to develop and train advanced predictive models.

2020-03-10 00:00:00 Read the full story…
Weighted Interest Score: 3.0804, Raw Interest Score: 2.2181,
Positive Sentiment: 0.1969, Negative Sentiment 0.0656

The Insights Beat: Stay On Course With Smart Data And Analytics Choices

As companies navigate through murky waters amid global health and economic headwinds, as a data and analytics leader, it may feel like there are many things that are out of your hands today. As you weather these conditions, it is still important to chart a steady course and be laser-focused on the parts you can control — which is to actively shape your enterprise’s ability to continue getting smarter about markets, competitors, products, and cust…
2020-03-10 19:22:13-04:00 Read the full story…
Weighted Interest Score: 3.0691, Raw Interest Score: 1.7070,
Positive Sentiment: 0.2276, Negative Sentiment 0.0284

AI Weekly: Coronavirus spurs adoption of AI-powered candidate recruitment and screening tools

As COVID-19 continues to spread — as of the time of writing (March 12), there were over 139,600 confirmed cases and over 5,100 deaths — companies are increasingly adopting alternatives to in-person job interviews and talent recruitment. Recruiters PageGroup and Robert Walters have announced plans to move some job interviews and interactions online, following on the heels of tech giants Amazon, Facebook, Google, and Intel.

At least a few have beg…
2020-03-13 00:00:00 Read the full story…
Weighted Interest Score: 2.9792, Raw Interest Score: 1.0860,
Positive Sentiment: 0.1614, Negative Sentiment 0.2201

Can We Judge An Machine Learning Model Just From Weights

Any machine learning solution comes with two primary challenges — accuracy and training time. There is always a trade-off between these two. There are places where you can’t trade accuracy, such as in the case of self-driving cars. That’s why we don’t see these cars on the road yet as the engineers are training the models with a vast number of features. However, there are applications where long hours of training time can’t be spared.

So, what if we just look at the weights and decide whether to train a model or not? This would dramatically reduce the computational costs of any ML pipeline. To study the prediction of the accuracy of a neural network given only its weights, the researchers from Google Brain propose a formal setting that frames this task.
2020-03-11 04:30:00+00:00 Read the full story…
Weighted Interest Score: 2.9790, Raw Interest Score: 1.5737,
Positive Sentiment: 0.0954, Negative Sentiment 0.0715

Should Early-Stage Startups Hire A Data Scientist?

With data scientists emerging as one of the most sought after positions, organisations across the world are looking to increase their data science capabilities. This has resulted in a deluge of jobs, with demands coming in from new startups as well. But do they need to hire a full-time data scientist early in its life cycle?

Indeed, there may be great uses for hiring data scientists, especially given that they can gather insights that can significantly contribute to overall business success. But without adequate customers and a proper data…
2020-03-16 05:30:34+00:00 Read the full story…
Weighted Interest Score: 2.9561, Raw Interest Score: 1.6603,
Positive Sentiment: 0.2025, Negative Sentiment 0.1822

A Brief History of Data Quality

The term “Data Quality” focuses primarily on the level of accuracy possessed by the data, but also includes other qualities such as accessibility and usefulness. Some data isn’t accurate at all, which, in turn, promotes bad decision-making. Some organizations promote fact checking and Data Governance, and, as a consequence, make decisions that give them an advantage. The purpose of ensuring accurate data is to support good decision-making in both…
2020-03-11 07:35:59+00:00 Read the full story…
Weighted Interest Score: 2.9191, Raw Interest Score: 1.7716,
Positive Sentiment: 0.2573, Negative Sentiment 0.1781

3i Infotech Launches AI Based Anti-Money Laundering Tool Called AMLOCK Analytics

3i Infotech Limited, a global Information Technology company, launched AMLOCK Analytics, its advanced anti-money laundering (AML) solution powered by Artificial Intelligence (AI) and Machine Language (ML), which enables banks and financial institutions to identify complex and hidden AML patterns. It helps organizations to meet their most critical challenge of managing high false positives and provides a holistic view of investigating an alert. AMLOCK Analytics uses various statistical methods and machine learning algorithms to derive analyses and predictions based on…
2020-03-12 06:17:01+00:00 Read the full story…
Weighted Interest Score: 2.8913, Raw Interest Score: 1.7270,
Positive Sentiment: 0.3621, Negative Sentiment 0.6128

The Advent And Scope Of AI Marketing In 2020 And Beyond

When it comes to bridging the existing gap between data science and its usage, targeting better marketing results, nothing beats the utilitarian nature of AI. While artificial intelligence alone is capable of sifting through humongous data sets for analyzing the relevant ones, AI marketing is slowly but steadily shaping up into a venture that comes with a host of benefits over the conventional ways of promoting a product or service.

2020-03-16 00:00:00 Read the full story…
Weighted Interest Score: 2.8694, Raw Interest Score: 1.2849,
Positive Sentiment: 0.4007, Negative Sentiment 0.0691

Data Security: How to move to a data-centric approach

Take a moment and think about some of the changes in computing over the last decade. The iPad debuted in 2010. Smartphones weren’t in everybody’s pockets yet. 4G was starting to branch out from major cities around the world. And we had only a taste of the Big Data.

The way everyone uses computers has fundamentally changed. It has redefined the role networks play in our lives. Employees are no longer limited to corporate systems and workstations to do their job. Students can connect to academic servers from any place on earth. The use of mobile devices, laptops, and tablets have now fostered the growth of the telecommute era.

It’s time for companies to embrace the data-centric approach. It’s the only way to adapt to technological advances and prime themselves for the changes to come.

2020-03-13 07:31:12+00:00 Read the full story…
Weighted Interest Score: 2.8563, Raw Interest Score: 1.6424,
Positive Sentiment: 0.1714, Negative Sentiment 0.4427

How AI will change the mobile app development industry

The tech world has been permeated by a plethora of disruptive technologies such as Artificial Intelligence, Machine Learning, AR/VR and so forth. The following post emphasizes on how the concept of AI seems to be revolutionizing the mobile app industry in one go!

We have reached 2020, a world that’s even more fast-paced and user-centric, a space that surely holds a wide range of promising trends for the industry ranging from chatbots to augmented reality. But above all, artificial intelligence steals the show due to i…
2020-03-09 04:29:44+00:00 Read the full story…
Weighted Interest Score: 2.8025, Raw Interest Score: 1.3155,
Positive Sentiment: 0.4063, Negative Sentiment 0.0967

Will Quantum Computing Define The Future Of AI?

Google, this week, has launched a new version of their TensorFlow framework — TensorFlow Quantum (TFQ), which is an open-source library for prototyping quantum machine learning models.

Quantum computers aren’t mainstream yet; however, when they do arrive, they will need algorithms. So, TFQ will bridge that gap and will make it possible for developers/users to create hybrid AI algorithms combining both traditional and quantum computing techniques…
2020-03-14 07:30:00+00:00 Read the full story…
Weighted Interest Score: 2.7679, Raw Interest Score: 1.3022,
Positive Sentiment: 0.2485, Negative Sentiment 0.4076

AI sector reacts to increased policy oversight and market uncertainty

Despite extreme economic uncertainty, policy makers continue attempts to regulate artificial intelligence (AI) as tech vendors adapt to the latest guidelines set by the European Commission last month, calling for a European AI strategy. Legal counsels are advising small fintechs and companies leveraging AI to utilise regulation for a competitive edge.

“The advice that we’re giving is to feed into any consultation process as soon as you can, because it can be used almost to get a competitive advantage because if you’re the person that’s feeding in and saying ‘this is how we think you should do this’ then you put yourself in a very good position,” says Mardi MacGregor, senior associate at Fox Williams. “Then you can make sure that the rules when they do come out are something that can work for you, but also sometimes you can make sure that your business is one of the businesses that succeeds under the new regulation.”

2020-03-12 00:00:00 Read the full story…
Weighted Interest Score: 2.7361, Raw Interest Score: 1.2358,
Positive Sentiment: 0.1696, Negative Sentiment 0.1938

Vida Diagnostics raises $11 million to diagnose lung diseases with AI

Vida Diagnostics, a provider of AI-powered lung imaging analysis tools, today announced that it has raised $11 million in a series C round. CEO Susan A. Woods said the funds will be used to accelerate the commercialization and expansion of the company’s product portfolio, which she says could address market deficits in the early assessment, monitoring, and treatment of lung disease.

“We are driven to continuously raise the standard of care for p…
2020-03-12 00:00:00 Read the full story…
Weighted Interest Score: 2.6757, Raw Interest Score: 1.0276,
Positive Sentiment: 0.1622, Negative Sentiment 0.1622

Importance Of Hypothesis Testing In Data Science

Data Science has two parts to it “Data” and “Science”. Alone both are having their individual meanings but when it is combined together “Data” gets power. Yes, you heard it right, but the question here is how “Data” gets power? Data alone is not interesting, it Is the interpretation and insights from the data that make it worthy. How to achieve that is another question pondering in our minds. So I would say statistics is the answer to this questi…
2020-03-12 12:30:00+00:00 Read the full story…
Weighted Interest Score: 2.6706, Raw Interest Score: 1.3891,
Positive Sentiment: 0.1736, Negative Sentiment 0.5209

Australian AI Startup Reejig Raises $2.2m

Reejig, a Sydney based startup that uses big data and AI to predict problems and opportunities in large complex workforces, today announced a $2.2 million first capital raise.

The company says the money will be used to scale its software as a service platform which connects various HR systems, collating the data on employees and potential employees to use in predictive analytics.

2020-03-10 11:47:58+11:00 Read the full story…
Weighted Interest Score: 2.6004, Raw Interest Score: 1.3686,
Positive Sentiment: 0.3193, Negative Sentiment 0.2281

Amazon’s AI predicts context from search queries

Amazon is using AI and machine learning to predict context from customers’ queries. In a preprint paper accepted to the ACM SIGIR Conference on Human Information Interaction and Retrieval scheduled to take place this month, Amazon researchers describe a system that predicts activities like “running” from queries like “Adidas men’s pants.” It could help to improve the quality of search results on Amazon.com, which could enhance the overall Amazon shopping experience.

As Adrian Boteanu, contributing author and Amazon Search customer experience applied scientist, explains in a blog post, most product discovery algorithms look for correlations between queries and products. By contrast, the researchers’ AI identifies the best matches depending on the context of use.
2020-03-12 00:00:00 Read the full story…
Weighted Interest Score: 2.5853, Raw Interest Score: 1.5102,
Positive Sentiment: 0.1798, Negative Sentiment 0.0000

Could Brexit open the gates to AI in the UK?

The UK is outwardly pursuing an adequacy decision from Europe regarding its current data protection and privacy regulations. However, the idea of eschewing outright assimilation with relevant EU laws is gaining traction as it provides an opportunity to sculpt a more appealing privacy framework.

As it stands, come 31 December 2020, the UK will have departed the EU, and the transition period which ensured the stability of continuing UK/EU regulation to bridge the exit will cease.

In conversation with Finextra Research, Miriam Everett, partner and global head of data and privacy at Herbert Smith Freehills, refers to a statement made by the UK Prime Minister Boris Johnson in February 2020 in which he alludes to the possibility of altering the application of current privacy laws under GDPR:

2020-03-09 11:01:00 Read the full story…
Weighted Interest Score: 2.4373, Raw Interest Score: 1.1207,
Positive Sentiment: 0.2537, Negative Sentiment 0.2396

Raising Deposits Amid Coronavirus Rate-Slashing and Stock Volatility

The U.S. has returned to the low-interest environment of pre-2018, following the Federal Reserve’s 50 basis point cut to the Fed funds rate, and its subsequent cut, both in an effort to contain the economic damage from the COVID-19 outbreak.

Bankers had been dusting off their 2017 playbooks to revisit deposit growth strategies for when the Fed funds rate was last in the 1%-1.25% range, and now must assess things in light of the Fed’s reduction in that key rate to a range of 0%-0.25%. When yields are low, consumers have little incentive to lock up funds for any length of time and are equally unlikely to move deposits for marginally higher returns.

Management has been focusing on protecting employees and serving people in a highly unusual period. But the economics of banking still march on. Banks and credit unions must be wary of falling into the old trap of resorting to undifferentiated, headline-grabbing rate promotions to accelerate deposit growth and stem deposit outflows. These competitive knee-jerk reactions can increase deposit costs and “hot money.” This is a pivotal time for financial institutions to move beyond myopic attempts at quick fixes.

Weighted Interest Score: 2.4308, Raw Interest Score: 1.3474,
Positive Sentiment: 0.2567, Negative Sentiment 0.2727


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