AI & Machine Learning News. 27, 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?


Former Google CEO Eric Schmidt: Let’s Start a School for A.I.

If you’re interested in becoming a technologist for the federal government, former Google CEO Eric Schmidt wants to teach you how to code.

According to OneZero, Schmidt has partnered up with former U.S. Secretary of Defense Robert O. Work to create a school for folks who want to become government coders. This U.S. Digital Service Academy would operate like a regular school, offering coursework and degree tracks, and focus on cutting-edge technology subjects such as cybersecurity and artificial intelligence (A.I.).

As OneZero points out, the federal government is very interested in technologists who can craft new innovations in A.I. “We are engaged in an epic race for A.I. supremacy,” the publication quotes Rick Perry, secretary of the Department of Energy, as telling an NSCAI conference in 2019. “As I speak, China and Russia are striving to overtake us. Neither of these nations shares our values or our freedoms.”

2020-07-23 00:00:00 Read the full story…
Weighted Interest Score: 1.8424, Raw Interest Score: 1.4056,
Positive Sentiment: 0.2743, Negative Sentiment 0.0686

CloudQuant Thoughts : The US needs to move fast to stay up with China on AI. There are few restrictions on the use of data or technology in China so we are fairly hamstrung from the get go.

Tech execs urge Washington to accelerate AI adoption for national security

Tech company CEOs may be heading to Washington, D.C. next week to take part in antitrust hearings in Congress, but this week high-profile executives from companies like Amazon, Microsoft, and Google gave the president, Pentagon, and Congress advice on how the United States can maintain AI supremacy over other nations. Today, the National Security Commission on AI released a set of 35 recommendations, ranging from the creation of an accredited university for training AI talent to speeding up Pentagon applications of AI in an age of algorithmic warfare.

The National Security Council on AI (NSCAI) was created by Congress in 2018 to advise national AI strategy as it relates to defense, research investments, and strategic planning. Commissioners include AWS CEO Andy Jassy, Google Cloud chief AI scientist Andrew Moore, and Microsoft chief scientist Eric Horvitz. Former Google CEO Eric Schmidt acts as chair of the group. Coming amid concerns over China’s rise as an economic and military power and AI’s increasing use in businesses and governments, the group’s recommendations may have a long-lasting impact on the United States government and the world.

2020-07-22 00:00:00 Read the full story…
Weighted Interest Score: 3.8346, Raw Interest Score: 1.4833,
Positive Sentiment: 0.1181, Negative Sentiment 0.2494

CloudQuant Thoughts : Whilst the tech CEOs sudden concern about America’s AI supremacy seems more of a “look over here” magic act distraction than genuine concern, it IS a MAJOR problem and it DOES need to be addressed. Whereas a lot of Washington’s concerns regarding the tech companies is based around “perceived” political bias, techs power does need to be addressed. I for one would tend to agree that the threat from Chinese AI dominance is greater than the threat from big Tech’s monopolies.

What You Need To Know About NVIDIA And University of Florida’s $70 million AI Partnership

The University of Florida and NVIDIA this week launched a collaborative initiative under which the two rolled out a plan to create the world’s fastest AI supercomputer in academia, providing 700 petaflops of AI performance.

The ambitious project is going to be worth $70 million, which includes a donation of $25 million from University of Florida alumnus and NVIDIA co-founder Chris Malachowsky, and $25 million in hardware, software, training and services from NVIDIA. The University of Florida will invest an extra $20 million for an AI-centric supercomputing and data centre.

“We’ve built a replicable, powerful model of public-private collaboration for everyone’s benefit,” stated Malachowsky, in an online event featuring leaders from both the UF and NVIDIA. A distinguished alumnus of UF, Malachowsky has served in a number of leadership roles at NVIDIA right from the beginning. He has not only been a leader at NVIDIA but also owns close to 40 patents, and worked extensively on integrated-circuit design and methodology.

2020-07-27 06:36:24+00:00 Read the full story…
Weighted Interest Score: 3.1959, Raw Interest Score: 1.4752,
Positive Sentiment: 0.2984, Negative Sentiment 0.0497

CloudQuant Thoughts : I am generally not one for corporate sponsorship in schools (high school food should not be sponsored by corporate organizations), but I do feel that Nvidia can help us to move US education to the forefront of AI and ML.

Study proves AI robots can boost social skills in children on autism spectrum

Artificial intelligence (AI) robots are changing the world we live in by helping us to not only expand our physical abilities but augment our behavioral patterns and boost our social skills as well. At present, approximately 1 in 54 children born in the USA is on the autism spectrum. Many of these children face a range of social, communication, and emotional challenges on a daily basis. Although traditional speech and behavior therapy can have a significant impact on a child’s development, these interventions are often extremely time-intensive and expensive. It is also very difficult to effectively apply a one-size-fits-all approach as each child experiences a unique set of challenges. Thankfully, the development of new AI in-home robotic companions is helping to supplement traditional therapies, enhancing a child’s development considerably.

A team of researchers based at the University of Southern California has successfully created a socially-assistive robot known as “Kiwi” to use in a study. The robot is able to teach math and social skills to children on the autism spectrum. By making use of video and audio data as well as eye contact and verbal dialogue, Kiwi can determine whether a child is immersed in a training activity or not. When it is detected that the child is not engaging in the activity, the robot will react accordingly and try to re-engage them for an extended period of time. During the initial testing phase of the robot, Kiwi managed to accurately predict a child’s level of engagement 90% of the time.
2020-07-21 11:47:21+00:00 Read the full story…
Weighted Interest Score: 1.8971, Raw Interest Score: 1.2577,
Positive Sentiment: 0.4509, Negative Sentiment 0.2373

CloudQuant Thoughts : If AI can help to engage and draw out children who struggle with communication that will be a huge positive for society and for those youngsters.

Facebook details the AI simulation tool it built to find bugs and vulnerabilities

Facebook today detailed Web-Enabled Simulation (WES), an approach to building large-scale simulations of complex social networks. As previously reported, WES leverages AI techniques to train bots to simulate people’s behaviors on social media, which Facebook says it hopes to use to uncover bugs and vulnerabilities.

In person and online, people act and interact with one another in ways that can be challenging for traditional algorithms to model, according to Facebook. For example, people’s behavior evolves and adapts over time and is distinct from one geography to the next, making it difficult to anticipate the ways a person or community might respond to changes in their environments.

2020-07-23 00:00:00 Read the full story…
Weighted Interest Score: 2.4510, Raw Interest Score: 1.1605,
Positive Sentiment: 0.0981, Negative Sentiment 0.2942

CloudQuant Thoughts : The idea that Facebook has built a walled off version of Facebook and set bots free to attempt to scam and target other users is fascinating from the white paper “WES systems are distinct because they turn lots of bots loose on something very close to an actual social media platform, not a mockup mimicking its functions.”


Bias in AI/ML

Four steps for drafting an ethical data practices blueprint – TechCrunch

In 2019, UnitedHealthcare’s health-services arm, Optum, rolled out a machine learning algorithm to 50 healthcare organizations. With the aid of the software, doctors and nurses were able to monitor patients with diabetes, heart disease and other chronic ailments, as well as help them manage their prescriptions and arrange doctor visits.

Optum is now under investigation after research revealed that the algorithm (allegedly) recommends paying more attention to white patients than to sicker Black patients.

Today’s data and analytics leaders are charged with creating value with data. Given their skill set and purview, they are also in the organizationally unique position to be responsible for spearheading ethical data practices. Lacking an operationalizable, scalable and sustainable data ethics framework raises the risk of bad business practices, violations of stakeholder trust, damage to a brand’s reputation, regulatory investigation and lawsuits.

Here are four key practices that chief data officers/scientists and chief analytics officers (CDAOs) should employ when creating their own ethical data and business practice framework.
2020-07-24 00:00:00 Read the full story…
Weighted Interest Score: 2.8346, Raw Interest Score: 1.4046,
Positive Sentiment: 0.3137, Negative Sentiment 0.2727

Researchers find evidence of bias in facial expression data sets

Researchers claim the data sets often used to train AI systems to detect expressions like happiness, anger, and surprise are biased against certain demographic groups. In a preprint study published on Arxiv.org, coauthors affiliated with the University of Cambridge and Middle East Technical University find evidence of skew in two open source corpora: Real-world Affective Faces Database (RAF-DB) and CelebA.

Machine learning algorithms become biased in part because they’re provided training samples that optimize their objectives toward majority groups. Unless explicitly modified, they perform worse for minority groups — i.e., people represented by fewer samples. In domains like facial expression classification, it’s difficult to compensate for skew because the training sets rarely contain information about attributes like race, gender, and age. But even those that do provide attributes are typically unevenly distributed.

2020-07-24 00:00:00 Read the full story…
Weighted Interest Score: 2.2425, Raw Interest Score: 0.9615,
Positive Sentiment: 0.0247, Negative Sentiment 0.1479

Leaders from Google, Adobe, and more talk benefits and bias at the Conversational AI Summit

“I’m extremely excited about the future of the intersection between conversational AI and the multitude of platforms that are being developed around these capabilities,” said Linden Hillebrand, VP Global Customer Success and Support at Cloudera during his opening remarks at the Transform 2020 Conversational AI Summit.

Over the course of the day tech giants from Adobe and Capital One to Google, Amazon, and Twitter spoke about how they’re using conversational AI to solve problems for their businesses in new and innovative ways.

The technology is being leveraged for both text chatbots and the NLP-powered voice assistants that are increasingly able to understand intent and offer a seamless, personalized user experience, helping automate the majority of customer interactions. But in most sessions, panelists emphasized that implementing these AI technologies also means tackling some of the bigger picture issues, including fairness, explainability, and elimination of bias.

Here’s a look at some of the top panels of the day, featuring leaders from Capital One, Google Assistant, and more.

2020-07-21 Read the full story…


RBC, Red Hat, Nvidia Deal

RBC moves AI unit to new private cloud platform

Royal Bank of Canada has moved applications under development at its artificial intelligence research unit Borealis AI to a high-performance private cloud infrastructure with support from Red Hat and Nvidia.

The bank says the new private cloud – which utilises Red Hat OpenShift and Nvidia’s DGX AI computing systems – has the ability to run thousands of simulations and analyse millions of data points in a fraction of the time than it could before.
2020-07-24 00:01:00 Read the full story…
Weighted Interest Score: 3.7552, Raw Interest Score: 1.6028,
Positive Sentiment: 0.5575, Negative Sentiment 0.0000

Royal Bank of Canada and Borealis AI announce new AI private cloud platform, developed with Red Hat and NVIDIA

RBC’s AI private cloud platform is the first-of-its-kind in Canada to deliver intelligent software applications and boost operational efficiency Royal Bank of Canada (RBC) and its AI research institute Borealis AI have partnered with Red Hat and NVIDIA to develop a new AI computing platform designed to transform the customer banking experience and help keep pace with rapid technology changes and evolving customer expectations.

As AI models become more efficient and accurate, so do the computational complexities associated with them. RBC and Borealis AI set out to build an in-house AI infrastructure that would allow transformative intelligent applications to be brought to market faster and deliver an enhanced experience for clients. Red Hat OpenShift and NVIDIA’s DGX AI computing systems power this private cloud system that delivers intelligent software applications and boosts operational efficiency for RBC and its customers.

2020-07-24 00:00:00 Read the full story…
Weighted Interest Score: 3.6095, Raw Interest Score: 1.5379,
Positive Sentiment: 0.4732, Negative Sentiment 0.0526

RBC taps Red Hat and Nvidia for new AI private cloud

Royal Bank of Canada (RBC) and its AI research institute Borealis have partnered with Red Hat and Nvidia. RBC says its trading execution and insights have already improved.

The partnership aims to create an in-house artificial intelligence infrastructure. The new build will allow “transformative intelligent applications” to reach the market faster. The bank has deployed Red Hat OpenShift and Nvidia’s DGX AI to power this new cloud platform. RBC claims its new cloud has the ability to run “thousands of simulations and analyse millions of data points.”

2020-07-27 09:30:15+00:00 Read the full story…
Weighted Interest Score: 3.0936, Raw Interest Score: 1.5926,
Positive Sentiment: 0.4191, Negative Sentiment 0.1676


How American Express Leverages ML To Achieve Lowest Card Fraud Rates In The World

The American Express Company, also known as Amex, is an multinational banking and financial services corporation headquartered in New York City. The company is a financial giant with 114 million cards in force, 64,000 employees worldwide and $1.24 trillion worldwide billed business.

For Amex, there are billions of transactions going through its system every month. With such a volume of card transactions, it is not just the dollar amount which is high, the network also generates massive amounts of data, including trillions of transactional data combinations which need to be analysed in almost real time

In such a situation, advanced techniques in machine learning and deep learning are essential, and the company uses them extensively in detecting and preventing frauds. But how does Amex do that on such a large scale?

2020-07-17 Read the full story…

10 Indian Startups That Are Leading The AI Race: 2020

The number of AI startups in India has increased tremendously over the years. Apart from being adopted in major industries, Artificial intelligence has become a way of doing business in other niche areas such as farming or even security. To recognise the unconventional startups in the AI space, Analytics India Magazine comes with a list of 10 such exceptional startups that are leading the AI race every year. In this year’s list, we have covered startups that are not more than 3 to 4 years old and have headquarters in India. Most of these startups are funded externally and are working hard to bring about exceptional transformation in the Indian tech ecosystem.

  • Ajna AI
  • Agricx
  • Expertrons
  • Innefu
  • Intello Labs
  • RayReach
  • Salesken AI
  • Spyne
  • Tericsoft
  • Vernacular AI

2020-07-22 Read the full story…

AI and Machine Learning Gain Momentum with Algo Trading & ATS Amid Volatility

An increasing number of capital markets firms are adopting machine learning and other artificial intelligence techniques to build algorithmic trading systems that learn from data without relying on rules-based systems.

With the hiring of data scientists, advances in cloud computing, and access to open source frameworks for training machine learning models, AI is transforming the trading desk. Already the largest banks have rolled out self-learning algorithms for equities trading.

Machine learning is a natural next step of algorithmic trading because machine learning identifies patterns and behaviors in historical data and learns from it,” said Robert Hegarty, managing partner, Hegarty Group, a consultancy focusing on financial services, technology, data, and AI/machine learning.

While traditional algorithms are created by programmers and quant strategists, these algorithms based on if/then rules do not learn on their own; they need to be updated. “With machine learning, you turn it over to the machine to learn the best trading patterns and update the algorithms automatically, with no human intervention,” said Hegarty. “That’s the big differentiator.”

2020-07-21 11:47:21+00:00 Read the full story…

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.

However, established businesses such as these are also the ones that can face the biggest challenges in driving value through machine learning due to technical debt, poor infrastructure and low-quality data, leading to higher costs of deployment as well as higher maintenance costs.

2020-07-14  Read the full story…

MSCI announces strategic alliance with Microsoft to accelerate innovation in the global investment industry

MSCI Inc. (NYSE: MSCI) and Microsoft Corp. have formed a strategic alliance to accelerate innovation among the global investment industry. By bringing together the power of Microsoft’s cloud and AI technologies with MSCI’s global reach through its portfolio of investment decision support tools, the companies will unlock new innovations for the industry and enhance MSCI’s client experience among the world’s most sophisticated investors, including asset managers, asset owners, hedge funds and banks.

MSCI logoInitially, the companies will focus on migrating MSCI’s existing products, data and services onto Azure as its preferred cloud platform in stages, starting with its Index and Analytics solutions followed by its Environmental, Social and Governance (ESG) products and ratings; Real Estate data and solutions; and MSCI’s risk analytics platform Beon. By modernizing MSCI’s data and analytics services and infrastructure, the companies will be able to deliver new capabilities which will help investors more swiftly and efficiently manage data and understand the drivers of risk and performance.

In addition, MSCI and Microsoft will explore collaboration opportunities to drive climate risk and ESG solutions, leveraging Microsoft’s Azure and Power Platform and MSCI’s ESG and climate solutions capabilities. This future collaboration, in line with both organizations’ commitment to sustainability, is intended to help investors better understand and interpret the business risks and opportunities that climate change brings.

2020-07-23 00:00:00 Read the full story…
Weighted Interest Score: 3.1789, Raw Interest Score: 1.6583,
Positive Sentiment: 0.7005, Negative Sentiment 0.1144

Fine Tune Bert For Text Classification

With the advancement in deep learning, neural network architectures like recurrent neural networks (RNN and LSTM) and convolutional neural networks (CNN) have shown a decent improvement in performance in solving several Natural Language Processing (NLP) tasks like text classification, language modeling, machine translation, etc.

However, this performance of deep learning models in NLP pales in comparison to the performance of deep learning in Computer Vision. One of the main reasons for this slow progress could be the lack of large labeled text datasets. Most of the labeled text datasets are not big enough to train deep neural networks because these networks have a huge number of parameters and training such networks on small datasets will cause overfitting.

Another quite important reason for NLP lagging behind computer vision was the lack of transfer learning in NLP. Transfer learning has been instrumental in the success of deep learning in computer vision. This happened due to the availability of huge labeled datasets like Imagenet on which deep CNN based models were trained and later they were used as pre-trained models for a wide range of computer vision tasks. That was not the case with NLP until 2018 when the transformer model was introduced by Google. Ever since the transfer learning in NLP is helping in solving many tasks with state of the art performance.

2020-07-20 18:30:39+00:00 Read the full story…
Weighted Interest Score: 3.6484, Raw Interest Score: 1.6565,
Positive Sentiment: 0.1232, Negative Sentiment 0.3017

Greater Acceptance of AI Translates to Lower Satisfaction Levels

The use of artificial intelligence to improve the customer experience has increased significantly as a result of COVID-19. However, while trust and acceptance of AI overall has increased, there has been a drop in satisfaction due to increased consumer expectations, particularly for later-stage interactions where a more humanized experience is preferred.

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 financial institutions and with each other, the demand for contactless or non-touch interfaces, such as chatbots, increases. This has forced organizations to find new ways to integrate advanced intelligence into the entire customer journey.

According to an Economist Intelligence Unit survey from March and April of 2020, 77% of bank executives believed the the ability to extract value from AI will sort the winners from the losers in banking. AI platforms were the second highest priority area of technology investment, behind only cybersecurity, according to the survey. The importance of AI adoption is only likely to increase in the post-pandemic era.

Unfortunately, the increased focus on the potential and use of AI has not been reflected in higher levels of satisfaction. Instead, satisfaction levels with AI have actually decreased since 2018.

2020-07-20 00:05:24+00:00 Read the full story…
Weighted Interest Score: 3.8699, Raw Interest Score: 1.4336,
Positive Sentiment: 0.4357, Negative Sentiment 0.1265

BigData Lake for Financial Services – Need to stress on Platform Governance

As Banks and Insurance firms have already embraced Data Lakes for their Artificial Intelligence and Machine learning capabilities, it is important to look for continuous Return on Investment on the platform.

If a Data Lake is not well maintained, it can turn into a swamp while finding usable data can confuse the data consumers. Most challenges can be solved by including an active platform governance of the Data Lake.

2020-07-26 15:14:01 Read the full story…
Weighted Interest Score: 4.3002, Raw Interest Score: 2.4514,
Positive Sentiment: 0.0845, Negative Sentiment 0.3381

Maintaining the Human Element in Machine Learning – Gigaom

BEGINS: WEDNESDAY, JUL 29, 202012:00 PM CDT
Join us for this free 1-hour webinar from GigaOm Research. The webinar features GigaOm analyst Andrew Brust and special guest Nicolas Omont from Dataiku, a leader across the entire AI lifecycle.

In this 1-hour webinar, you will discover:

  • The types of ML models where the human element is most critical
  • How non-empirical factors figure into the model fairness equation
  • The respective roles of data scientists and ML engineers in the ML monitoring process
    Automated and human components of model explainability

Machine learning (ML) and ML operations platforms are becoming increasingly popular and sophisticated. That’s a good thing, as it transforms AI initiatives from science projects to rigorous engineering efforts. But with such platforms comes the temptation of automation, scripting the whole ML process, not just optimizing models, but monitoring their drift in accuracy and retraining them. While some automation is good, humans play a critical role.

Elements of fairness are contextual and involve tradeoffs. Changes in data may require retraining or restructuring a model’s features, depending on circumstances and current events. All of this requires human judgment, carefully integrated with automated management and algorithmic learning. Humans have to be part of the workflow, included in the feedback loop, and involved in the process.

2020-07-29 12:00:24-05:00 Read the full story…
Weighted Interest Score: 3.5104, Raw Interest Score: 1.9011,
Positive Sentiment: 0.2852, Negative Sentiment 0.1901

The Largest CAD Dataset Released With 15M Designs

In an attempt to automate industrial designing, researchers from Princeton University and Columbia University introduced a large dataset of 15 million two-dimensional real-world computer-aided designs — SketchGraphs. Along with that to facilitate research in ML-aided design, they also launched an open-source data processing pipeline.

Introduced during the International Conference on Machine Learning, SketchGraphs is aimed to train the artificial intelligence machine with this large dataset, in order to expertise it to assist humans in creating CAD models. In a recent paper, researchers revealed that each of the CAD sketches is represented with a geometric constraint graph and the understanding of the line and shape sequence in which the design was initially created. This will enable the predictions of what is going to be designed next.

There have been many CAD data sets available by voxel or mesh, which have allowed users to work on sampling realistic 3D shapes for creating CAD models. However, these models are usually not modifiable in parametric design settings and thus not preferred for engineering workflows. SketchGraphs, on the other hand, approaches parametric modelling instead of focusing on 3D shape modelling.

2020-07-25 07:30:00+00:00 Read the full story…
Weighted Interest Score: 3.3669, Raw Interest Score: 1.3736,
Positive Sentiment: 0.1282, Negative Sentiment 0.0183

Guided Labeling Episode 2: Label Density

The Guided Labeling series of blog posts began by looking at when labeling is needed — i.e., in the field of machine learning when most algorithms and models require huge amounts of data with quite a few specific requirements. These large masses of data need to be labeled to make them usable. Data that is structured and labeled properly can then be used to train and deploy models.

In the first episode of our Guided Labeling series, An Introduction to Active Learning, we looked at the human-in-the-loop cycle of active learning. In that cycle, the system starts by picking examples it deems most valuable for learning, and the human labels them. Based on these initially labeled pieces of data, a first model is trained. With this trained model, we score all the rows for which we still have missing labels and then start active learning sampling. This is about selecting or re-ranking what the human-in-the-loop should be labeling next to best improve the model.

There are different active learning sampling strategies, and in today’s blog post, we want to look at the label density technique.

2020-07-24 07:35:35+00:00 Read the full story…
Weighted Interest Score: 3.3176, Raw Interest Score: 1.4785,
Positive Sentiment: 0.0786, Negative Sentiment 0.0315

Trucking Industry in Early Stage of Adopting AI to Help Move Freight

AI is making inroads in the trucking industry, where it is used to optimize loads and drive predictive maintenance, thereby lowering costs.

Coyote Logistics had developed a network of 35,000 contract carriers and a range of software applications designed to help deliver short-term trucking services to shipping companies. Customer UPS liked it so much they bought the company, paying $1.8 billion in 2015.

Today the UPS Supply Chain Solutions unit is considered a leader by Gartner in what it calls the Third-Party Logistics market. In recent news, Coyote released an update to its Dynamic Route Optimization program that aims to streamline operations and reduce uncertainty for carriers by planning consistent loads on optimized routes.

It’s helping solve problems for truckers. “Like all carriers, inconsistent load volume, rates and schedule gaps are significant sources of stress that are exacerbated by market volatility,” stated Eric Lewis, VP of Operations at Ed Lewis Trucking, in a recent Coyote press release. “Dynamic Route Optimization from Coyote has helped us remove uncertainty from our weekly operations by strategically stringing shipments together so we can keep our fleet full and moving, while providing our drivers the amount of miles per week they were promised.”

2020-07-23 21:30:02+00:00 Read the full story…
Weighted Interest Score: 3.1027, Raw Interest Score: 1.3734,
Positive Sentiment: 0.2289, Negative Sentiment 0.2146

AI Weekly: The promise and shortcomings of OpenAI’s GPT-3

Facebook continues to face fallout from bias and discrimination issues, with multiple news outlets reporting that Instagram’s content moderation algorithm was 50% more likely to flag and disable the accounts of Black users than White users. Facebook and Instagram are now creating teams to examine how algorithms impact the experiences of Black and Latinx users, as well as users from other specific groups.

Also this week: Executives from Amazon, Google, and Microsoft gave leaders in Washington more than 30 recommendations to help the U.S. maintain an edge over other nations in AI. Recommendations include recruiting AI practitioners for a reserve corps that would do part-time government work and creating an accredited academy for the U.S. government to train AI talent.

But arguably the biggest story this week was the beta release of GPT-3, a language model capable of a great range of tasks, like summarization, text generation to write articles, and translation. Tests made especially to analyze GPT-3 found it can also complete many other tasks, like unscrambling words and using words it has only seen defined once in sentences.

2020-07-24 00:00:00 Read the full story…
Weighted Interest Score: 3.0983, Raw Interest Score: 1.4411,
Positive Sentiment: 0.1658, Negative Sentiment 0.2168

Safehub taps building-mounted motion sensors and AI to detect earthquakes

Safehub, whose platform enables businesses to monitor their buildings for signs of earthquakes, today closed a $5 million seed round. The company says it will use the capital to accelerate deployment to Fortune 500 customers as it expands its engineering team.

A recent FEMA study pegged U.S. losses from earthquakes at $4.4 billion per year. (Each year, there are on average about 15 earthquakes with a magnitude of 7 or greater, strong enough to cause damage in the billions and significant loss of life.) In spite of the risk, more than 60% of U.S. small businesses don’t have a formal emergency-response plan and fail to back up their sensitive data offsite.

2020-07-23 00:00:00 Read the full story…
Weighted Interest Score: 2.5695, Raw Interest Score: 1.3154,
Positive Sentiment: 0.0897, Negative Sentiment 0.5680

The Journey to Effective Data Management in High Performance Computing (HPC)

Imagine a simple interface for data search across an organization’s local and cloud storage. The search would return relevant data types, their location, and automatically extracted metadata. From there, advanced analytics could be performed in a serverless environment, and scale seamlessly to the cloud as needed. Results files would be presented in an interactive, configurable, and shareable format. Large raw data files could be transferred to collaborators in an efficient, parallel format over high speed, low latency connections.

While this visionary solution sounds like an incredible way to advance research and take advantage of diverse datasets, such a solution does not exist. When it comes to managing petascale datasets, most organizations don’t know where to start.

2020-07-23 00:00:00 Read the full story…
Weighted Interest Score: 2.5614, Raw Interest Score: 1.3930,
Positive Sentiment: 0.2097, Negative Sentiment 0.1648

A researcher created a ‘Weird A.I. Yancovic’ algorithm that generates parodies of existing songs, and now the record industry is accusing him of copyright violations

A researcher created a machine learning model that creates new lyrics to existing songs, much in the same way that parody singer Weird Al Yankovic does.

But the algorithm, dubbed “Weird A.I. Yancovic,” has landed creator Mark Riedl in hot water with the record industry, according to a Vice report.

Twitter took down one of his videos, which featured the instrumental section of Michael Jackson’s “Beat It,” after a coalition of major record compan…
2020-07-24 00:00:00 Read the full story…
Weighted Interest Score: 2.4825, Raw Interest Score: 0.9239,
Positive Sentiment: 0.0637, Negative Sentiment 0.1274

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.

A data-led IT firm can utilize artificial intelligence (AI) to create one-on-one conversations with its clients. This can effectively be achieved by making use of both first- and third-party data in order to gain a greater understanding of the client’s unique needs. In IT, complex algorithms can assist IT consultants with infrastructure planning and design for specific projects. High-level data-led insights such as these make it possible to make pertinent decisions without any significant human intervention. Many business owners strive towards being able to set goals and plan at the same time. Data-led IT solutions can make it considerably easier for business entities to achieve just this.
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

Quantexa raises $64.7 million for AI platform that extracts insights from big data

Big data analytics startup Quantexa today closed a $64.7 million financing round at a valuation “well north of a quarter billion dollars,” which a spokesperson told VentureBeat will be put toward accelerating the company’s product roadmap and expansion in Europe, North America, and Asia Pacific regions. It comes after a year in which Quantexa landed customers like SBC, Standard Chartered Bank, and OFX and expanded the availability of its platform to more than 70 countries.

Enterprises have multiple data buckets to wrangle — upwards of 93% say they’re storing data in more than one place — and some of those buckets inevitably become underused, partially used, or forgotten. A Forrester survey found that between 60% and 73% of all data within corporations is never analyzed for insights or larger trends, while a separate Veritas report found that 52% of information stored by organizations is of unknown value. The opportunity cost of this unused data is substantial, with the Veritas report pegging it as a cumulative $3.3 trillion by the year 2020 if the current trend holds.

020-07-23 00:00:00 Read the full story…
Weighted Interest Score: 2.2853, Raw Interest Score: 1.4710,
Positive Sentiment: 0.1313, Negative Sentiment 0.2364

Power Plant Energy Output Prediction: Weekend Hackathon #13

Weekend hackathons are fun, aren’t they! In our last weekend hackathon, we introduced a new and unique problem statement using UCI open dataset. But, we were big-time disappointed as some of the participants ended up probing the leaderboard. However, we decided to host an open UCI dataset competition again this weekend. So In this weekend hackathon, we have trained a machine learning model to perturb the target column instead of manually adding the noise. Yes, you heard it right, In this hackathon, we are challenging all the MachineHackers to capture our leaderboard and prove their mettle by competing against MachineHack’s AI.

2020-07-24 11:55:00+00:00 Read the full story…
Weighted Interest Score: 2.2585, Raw Interest Score: 0.9819,
Positive Sentiment: 0.0298, Negative Sentiment 0.2975

StuffThatWorks raises $9 million to build an AI-powered, crowdsourced medical knowledge platform

StuffThatWorks, a startup leveraging AI, crowdsourcing, and machine learning to match patients with treatments, today closed a $9 million seed round. CEO Yael Elish says the proceeds will be used to accelerate go-to-market efforts as the company’s platform experiences pandemic-driven growth.

In response to the worsening global health crisis, patients and providers have sought out digital health and medical solutions to problems induced by COVID-19. In regions under lockdown, remote visits are now one of the key ways for patients to connect with specialists. Moreover, data and health platforms that pair providers with support data analytics have become critical for information-sharing, research, and analysis.

StuffThatWorks was cofounded by Elish, a founding member and former head of product at Google-owned Waze; CTO Ron Held, previously on the Israel Defense Force’s intelligence team; and chief data scientist Yossi Synett. Elish spent years helping a family member cope with a medical condition that also took a toll on her family’s life. After months of online research, he discovered a medical treatment that was more effective than what she’d been prescribed.

This experience inspired Elish to launch StuffThatWorks, a portal that taps AI to enable patients to share personal treatments and discover options that work best for them.

2020-07-23 00:00:00 Read the full story…
Weighted Interest Score: 2.0029, Raw Interest Score: 1.0566,
Positive Sentiment: 0.2026, Negative Sentiment 0.2461

RetrieveGAN AI tool combines scene fragments to create new images

Researchers at Google, the University of California at Merced, and Yonsei University developed an AI system — RetrieveGAN — that takes scene descriptions and learns to select compatible patches from other images to create entirely new images. They claim it could be beneficial for certain kinds of media and image editing, particularly in domains where artists combine two or more images to capture each’s most appealing elements.

AI and machine learning hold incredible promise for image editing, if emerging research is any indication. Engineers at Nvidia recently demoed a system — GauGAN — that creates convincingly lifelike landscape photos from whole cloth. Microsoft scientists proposed a framework capable of producing images and storyboards from natural language captions. And last June, the MIT-IBM Watson AI Lab launched a tool — GAN Paint Stu…
2020-07-22 00:00:00 Read the full story…
Weighted Interest Score: 1.9369, Raw Interest Score: 1.0244,
Positive Sentiment: 0.1938, Negative Sentiment 0.1107

What Is Scalability and How Do You Build for It? 6 Engineers Weigh In.

When you think of scalability, think of Black Friday.

At least that’s what Alex Bugosh, a principal software engineer at Jellyvision, does.

“The classic problem of scalability is that of an e-commerce system,” Bugosh said. “It needs to be able to handle the traffic of Black Friday while being economical enough to run the rest of the year.”

An e-commerce system that lags or experiences downtime can impact sales and user experience dramatically,…
2020-07-21 00:00:00 Read the full story…
Weighted Interest Score: 1.8267, Raw Interest Score: 1.2819,
Positive Sentiment: 0.3128, Negative Sentiment 0.2024

Executive Interview: Perry Lea, Book Author, Entrepreneur, Director of Architecture: Microsoft

Perry Lea is a 30-year veteran technologist. He spent over 20 years at Hewlett-Packard as a chief architect and distinguished technologist of the LaserJet business. He then led a team at Micron as a technologist and strategic director, working on emerging compute using in-memory processing for machine learning and computer vision. Perry’s leadership extended to Cradlepoint, where he pivoted the company into 5G and the Internet of Things (IoT). Soon afterwards, he co-founded Rumble, an industry leader in edge/IoT products. He was also a principal architect for Microsoft’s Xbox and xCloud and today is a director of architecture for Microsoft. Perry has degrees in computer science and computer engineering, and an EngrD in electrical engineering from Columbia University. He is a senior member of the Institute of Electrical and Electronics Engineers (IEEE) and a senior member/ distinguished speaker of the Association for Computing Machinery (ACM). He holds 50 patents, with 30 pending.
2020-07-23 21:30:09+00:00 Read the full story…
Weighted Interest Score: 1.8259, Raw Interest Score: 1.1920,
Positive Sentiment: 0.1587, Negative Sentiment 0.1098


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