AI & Machine Learning News. 15, June 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?


Synthesia.io – Create your own AI Video as easily as sending an email…

CloudQuant Thoughts : I pulled this company out of the post below (8 AI Companies Generating Creative Advertisign Content). Synthesia.io appear to have created a number of products using deepfake-like technology. I particularly like their phone app‘ to ‘smart office based rep‘ video, I could use that for my instruction videos and Zoom meetings! Translate all of your instructional videos into multiple languages using one service. Very impressive.

8 AI Companies Generating Creative Advertising Content

After the introduction of Generative Adversarial Networks (GANs) in 2014, a whole new era for AI image synthesis began. The latest GAN architectures can generate high-resolution, realistic, and colorful images that are almost impossible to distinguish from the real photographs. So, why spend time on exhausting and expensive photoshoots and video-shoots, if you can use an image or video of an automatically generated AI model who is the perfect fit for your brand? That’s the solution that the most technologically advanced marketing teams are turning to nowadays.

However, AI capabilities are not limited to the generation of visual content. The advances in natural language processing (NLP) and natural language generation (NLG) of the last few years have made artificial intelligence a part of copywriter teams. The marketing messages generated with AI-driven solutions are not only plausible but also data-driven – the text can reflect the brand’s voice and be tailored to specific audiences.

In this article, we feature companies that are leveraging cutting-edge AI research for generating marketing visuals and ad copy.
2020-06-08 15:39:29+00:00 Read the full story…
Weighted Interest Score: 3.0447, Raw Interest Score: 1.4495,
Positive Sentiment: 0.2675, Negative Sentiment 0.0863

CloudQuant Thoughts : In other top articles this month we cover how AI seems to be struggling to make a space in writing and journalism. However, in the world of digital video, particularly the creation of artificial humans, AI is racing ahead.

OpenAI Releases Commercial API That It Earlier Deemed Too Dangerous

One of the prominent AI research labs, OpenAI, has recently launched the beta version of an API for accessing new AI models developed by the company. The API allows searching over documents based on the natural-language meaning of queries rather than keyword matching.

With the advancement of machine learning and other emerging technologies, the ultimate goal of this journey is to achieve Artificial General Intelligence (AGI). According to the researchers, the API will serve as a revenue source to help them cover costs in further AI research as well as assist in advancing the technology and making it usable in the real world.

The API is designed to be simple for anyone to use but also flexible enough to make machine learning teams more productive. While providing any text prompt, the API will yield a text completion and attempts to match the pattern that the user provides.
2020-06-15 12:30:00+00:00 Read the full story…
Weighted Interest Score: 3.2557, Raw Interest Score: 2.0017,
Positive Sentiment: 0.2812, Negative Sentiment 0.1323

CloudQuant Thoughts : Shameless fake PR or genuine concern. With last week’s article that Microsoft were laying off dozens of journalists at MSN and Microsoft News to replace them with AI one would think that the writing was on the wall. OpenAI claimed to be worried that their neural network would be used by extremist groups and for spam & fishing. Well, if your bar to beat is Nigerian Princes and Tweets from Russian Operatives then your product is probably not that great. Try it out yourself.

AI Can Save Journalism, or AI Will Replace Journalists

AI can potentially save journalism, or AI is going to take over some writing and take away even more jobs – which is it?

In the optimistic view, the future of journalism could lie in AI, according to a new book from Francesco Marconi, a professor of journalism at Columbia University in New York, Newsmakers, Artificial Intelligence and the Future of Journalism. He was head of the media lab at the Wall Street Journal and the Associated Press, one of the largest news organizations in the world.

The journalism world is not keeping pace with new technologies, so newsrooms need to use what AI can offer and come up with a new business model, suggests Patrick White, a professor of journalism at the University of Quebec, writing about Marconi’s book in The Conversation. White was the founder of the Quebec edition of Huffington Post, which is managed from 2011 to 2018. He has a range of experience in Canadian print and television journalism.
2020-06-11 21:30:21+00:00 Read the full story…
Weighted Interest Score: 3.4808, Raw Interest Score: 1.4087,
Positive Sentiment: 0.0927, Negative Sentiment 0.1112

CloudQuant Thoughts : If you believe journalism is the cold hard reporting of facts then maybe AI can be a better journalist (assuming we don’t load it with too many biases). In my opinion, the writing in good journalism has to engage and move the reader, something I have yet to see an AI algo acheive. Go on.. move me.

Work Underway to Assess and Rate AI Model Transparency

We hold these truths to be self-evident: a machine learning model is only as good as the data it learns from. Bad data results in bad models. A bad model that identifies butterflies when it should be recognizing cats is easy to spot.

Sometimes a bad model might be more difficult to spot. If data scientists and ML engineers that trained the model selected a subset of available data with an inherent bias, the model results could be skewed. Or the model might not have been well-trained enough, or it could have issues with overfitting or underfitting.

If there are many ways the model could fail, how are we to trust the model? asks a recent account in Forbes on AI transparency and explainability by Ronald Schmelzer.

2020-06-11 21:30:23+00:00 Read the full story…
Weighted Interest Score: 4.9214, Raw Interest Score: 1.9890,
Positive Sentiment: 0.4167, Negative Sentiment 0.1894

CloudQuant Thoughts : We are still very much in the “Human Review” stage of AI, people outside the industry think you just throw data at AI and it works everything out itself. Reviewing the AI’s decision at the end or parallel running the process with the original human process tends to make us feel better about the outcome. Unfortunately we humans have biases and we are often the source of the bias expressed by our AI algos.

Covid-19 Crisis Unlikely to Affect Impact Investing

Most impact investors recently surveyed by the Global Impact Investing Network (GIIN) expect to maintain or boost their commitment to impact investments this year in response to the coronavirus pandemic, and most plan to stick to strategies focused on addressing the U.N.’s sustainable development goals.

The responses were an addendum to the GIIN’s 10th annual survey of impact investors, which was released Thursday morning. The survey was conducted in February and March with 294 respondents, before most sheltering-in-place orders took hold, and the extent of the pandemic’s impact on global economies was not yet known. To better understand how the Covid-19 crisis would affect attitudes, the GIIN reached out later in the spring, receiving responses from 122 investors.

“One of the things that’s really clear about this crisis is that it underscores the need for investment capital to play a role in driving positive impact,” says Amit Bouri the GIIN’s CEO and co-founder. “As the dust settles on the shock of the crisis, and people have more energy to think about how to build back and recover, impact investing as an approach and the thinking behind it, will have a much greater interest among investors.”

2020-06-11  Read the full story…

CloudQuant Thoughts :  GIIN is a non-profit which promotes social & environmental impact alongside a financial return. This is another arm of ESG (Environmental, Social and Governance), a huge current driver of investment as data scientists seek useful alternative data sources. Part of CloudQuant’s role is to curate alternative data sources, we also test them and make the data and code used available for reproduction of the test results. Head over to our Data Catalog for more information.


BIG THREE DROP OUT OF FACIAL RECOGNITION MARKET

Three Big Tech Players Back Out of Facial Recognition Market

In the span of 72 hours, both IBM and Amazon backed out of the facial recognition business this week. It’s a chess match on the geopolitical playing board, with AI ethics and data bias in play. IBM moved first, closely followed by Amazon. (And then two days later Microsoft announced its intention to also exit the market; see below.)

The moves came after demonstrations were held across both the US and the world, in response to police mistreatment of black Americans. Facial recognition software has been called out by privacy and AI ethics groups as having higher error rates for people of color.
2020-06-11 21:30:59+00:00 Read the full story…
Weighted Interest Score: 2.3890, Raw Interest Score: 1.0815,
Positive Sentiment: 0.0755, Negative Sentiment 0.4904

COVER STORY: IBM, Amazon and Microsoft Abandon Law Enforcement Face Recognition Market

Three global tech giants — IBM, Amazon, and Microsoft — have all announced that they will no longer sell their face recognition technology to police in the USA, though each announcement comes with its own nuance.

The new policy comes in the midst of ongoing national demonstrations in the US about police brutality and more generally the subject of racial inequality in the country under the umbrella of the Black Lives Matter movement.

While the t…
2020-06-15 01:44:39+10:00 Read the full story…
Weighted Interest Score: 2.1902, Raw Interest Score: 0.9290,
Positive Sentiment: 0.0555, Negative Sentiment 0.3050


Researchers find racial discrimination in ‘dynamic pricing’ algorithms used by Uber, Lyft, and others

A preprint study published by researchers at George Washington University presents evidence of social bias in the algorithms ride-sharing startups like Uber, Lyft, and Via use to price fares. In a large-scale fairness analysis of Chicago-area ride-hailing samples — made in conjunction with the U.S. Census Bureau’s American Community Survey (ACS) data — metrics from tens of millions of rides indicate ethnicity, age, housing prices, and education influence the dynamic fare pricing models used by ride-hailing apps.

The idea that dynamic algorithmic pricing disproportionately — if unintentionally — affects certain demographics is not new. In 2015, a model used by the Princeton Review was found to be twice as likely to charge Asian Americans higher test-preparation prices than other customers, regardless of income. As the use of algorithmic dynamic pricing proliferates in other domains, the authors of this study argue it’s crucial that unintended consequences — like racially based disparities — are identified and accounted for.

“When machine learning is applied to social data, the algorithms learn the statistical regularities of the historical injustices and social biases embedded in these data sets,” paper coauthors assistant professor Aylin Caliskan and Ph.D. candidate Akshat Pandey told VentureBeat via email. “With the starting point that machine learning models trained on social data contain biases, we wanted to explore if … [the] algorithmic ride-hailing data set exhibits any social biases.”
2020-06-12 00:00:00 Read the full story…
Weighted Interest Score: 2.8205, Raw Interest Score: 1.1650,
Positive Sentiment: 0.0601, Negative Sentiment 0.2642

It’s Time to Adopt a “Data Quality Over Quantity” Mindset

Today’s marketing leaders strive to collect, process, and activate large amounts of data in an effort to improve data-driven decision-making, execute personalization at scale and activate a myriad of use cases.

The power of data and analytics is well documented and cannot be overemphasized. Highly data-driven organizations are three times more likely to report improvement in their decision-making, according to PwC research. However, too many marketers are focused on the quantity, rather than the quality, of the data they are working with. Companies will not reap the maximum benefit of data if they are working from a foundation of inaccurate information—or, worse, they will do more harm than good by making choices based on faulty intel. The problem becomes more severe when bad data is used to train machine learning models, since the resulting models are only as good as the data used to train them; without a good dataset, predictive analytics becomes merely calculated randomness.

2020-06-10 11:00:00+00:00 Read the full story…
Weighted Interest Score: 2.5210, Raw Interest Score: 1.3280,
Positive Sentiment: 0.2353, Negative Sentiment 0.3866

ESG Fund Ratings: Not Perfect, but Still Valuable – ESG data is more robust than many critics think, and it’s improving over time.

Critics of environmental, social and governance fund ratings often cite numerous reasons as to why the ratings lack validity. While the ratings aren’t perfect, we explore some of the reasons why we believe they are worthwhile and how they may continue to improve.

One common argument regarding the validity of ESG ratings is that there are hundreds of ESG data, analytics and research providers, and that their scores are sometimes conflicting, making it difficult to draw conclusions. The reality is that there are only a handful of prominent ESG research firms, most notably Sustainalytics and MSCI.

These firms have long played an important role in gathering and assessing information about companies’ ESG practices. This has been and remains a considerable challenge. Company disclosures on ESG practices have always been voluntary, are rarely audited, and are not standardized.
2020-06-09 00:00:00 Read the full story…
Weighted Interest Score: 2.7071, Raw Interest Score: 1.5094,
Positive Sentiment: 0.2012, Negative Sentiment 0.3019

AI and Human Operators Combine to Train Robots

Ever since the first crude cinematic robot first arrived on the scene in the 1927 movie Metropolis, humans have been fixated on the fear that they would come to rue the day artificial intelligence (AI) was summoned into existence and that, eventually, the masters would become slaves to these superior automated brains.

But from the vantage point of nearly a full century later, it seems like there might be another way this could go in which humans and AI — and AI’s close cousin machine learning (ML) — might find a way forward with a symbiotic relationship that stands to benefit humanity and not end in the subjugation or ultimate destruction of the human race.

Maybe we’ve come to the point in time when AI and human operators can combine forces to build and train better robots.

2020-06-12 07:30:42+00:00 Read the full story…
Weighted Interest Score: 2.4974, Raw Interest Score: 1.2241,
Positive Sentiment: 0.3350, Negative Sentiment 0.3350

AI Tool Turns Blurry Human Photo Into Realistic Computer-Generated HD Faces

Duke University researchers have announced that they have developed an artificial intelligence-based tool that can turn blurry and unrecognisable images of people’s faces into perfect computer-generated portraits in high definition.

According to the reports, traditional methods can only scale up a human face image up to eight times than its original resolution; however, the researchers from the Duke University have developed this AI tool called PULSE, which can create a realistic-looking image which is 64 times the resolution of the input photo. This tool searches through artificial intelligence-generated high-resolution faces images as an example and analyses facial features like fine lines, eyelashes and stubble to match ones that look similar to the input image after actual size compression.

2020-06-15 07:39:58+00:00 Read the full story…
Weighted Interest Score: 2.0882, Raw Interest Score: 1.0886,
Positive Sentiment: 0.0294, Negative Sentiment 0.1177

What Are The Markers Of A Genuine Data Scientist?

A data scientist is a professional who deals with a colossal amount of information, analyses it, and helps organisations to derive actionable insights from data. However, with a high median base salary and being the sexiest job of the century, the role of a data scientist is getting a lot of attraction from individuals as well as businesses. In fact, according to Glassdoor research, the average base pay of a data scientist is ₹988K per year, and therefore many professionals are marketing themselves as data scientists despite lacking actual skills with data.

Alongside, the job profile of a data scientist is quite complex, and therefore many of the business leaders don’t understand the core of it. Consequently, many think that any professional who deals with data are data scientists. However, that’s not the case. To be a real data scientist one needs to have much more skill sets apart from just knowing the data. Also, with inadequate skill sets, a so-called data scientist can make ineffective data models, which in turn would affect the company’s bottom line.
2020-06-14 14:30:00+00:00 Read the full story…
Weighted Interest Score: 4.1958, Raw Interest Score: 2.0839,
Positive Sentiment: 0.2378, Negative Sentiment 0.2098

These 12 artificial intelligence startups are poised for success, particularly in a post-COVID world, according to experts

Demand for artificial intelligence technology has been growing over the last five years and is poised to grow even faster due to the COVID-19 crisis, experts say.
As businesses adapt to major changes caused by the crisis, they’ll likely turn to AI technology to streamline operations and become more efficient, analysts and investors said.
AI technology has helped businesses with tasks like making long-term sales growth projections to automating routine, time-consuming tasks.
Two VCs say industry-specific AI software is especially poised to see strong growth and increased investment.
Here are 12 startups that well-positioned to grow in a post-COVID world, including Tempus, Replicant, and Olive.

2020-06-14 00:00:00 Read the full story…
Weighted Interest Score: 4.2888, Raw Interest Score: 1.7525,
Positive Sentiment: 0.2861, Negative Sentiment 0.3219

DataRobot Adds to its AI Toolbox

DataRobot, the automated machine learning software vendor, continued its string of acquisitions this week with a deal to buy Boston Consulting Group’s AI technology platform. The companies also announced a strategic partnership that would combine consulting services with DataRobot’s intellectual property.

The AI acquisition and partnership seek to address the growing number of unsuccessful enterprise AI deployments. Missing is the ability to build, deploy and monitor machine learning models that produce actual results and return on investment.

Hence, the partners said Tuesday (June 9) they will collaborate to help customers build “industrial-grade” AI platforms. To that end, high-flying DataRobot will acquire the business consultant’s Source AI technology. The platform is designed to free data scientists to write restriction-free code used that combines human and technical expertise.

2020-06-09 00:00:00 Read the full story…
Weighted Interest Score: 3.9746, Raw Interest Score: 2.1204,
Positive Sentiment: 0.1272, Negative Sentiment 0.1272

Banking industry affects due to Covid-19

The term Artificial Intelligence is nothing but a computer program embedded with aspects of human intelligence i.e the ability to think like human beings. In the upcoming years, AI along with machine learning, data analytics and deep learning would be a major thing across industries. One such industry that has been revolutionized by AI is the financial sector. Amongst the financial sector, AI in banking will become more prominent given the current situations of Covid-19. AI technology has transformed the financial sector in many ways such as:
2020-06-12 05:57:28 Read the full story…
Weighted Interest Score: 3.9462, Raw Interest Score: 1.6934,
Positive Sentiment: 0.3474, Negative Sentiment 0.6513

Should a Data Scientist Know How to Code?

A closer look into different types of data scientists

A data scientist can be many things, and a coder could one of them. Over the course of my career in data science, I have seen a wide array of professionals using the tiniest amount of coding. But on the other hand, I have seen people write books of code to explain their model. Really, what it comes down to is what type of…
2020-06-15 00:48:38.522000+00:00 Read the full story…
Weighted Interest Score: 3.8251, Raw Interest Score: 2.0767,
Positive Sentiment: 0.0913, Negative Sentiment 0.0228

Emergence of Conversational Agents in Investment Banking

Investment banks continue to be under pressure due to regulatory changes, declining revenues, and rising costs. Revenues generated by 12 biggest investment banks from trading and advisory operations are down by 11% for first six months of 2019, according to Financial Times article. COVID-19 pandemic will impact investment banks due to significantly reduced economic activity globally, and a potential economic recession may put pressure on revenues of investment banks. In this context, a majority of the Investment Banks are increasingly adopting technology driven business innovations as one of the key strategies to achieve business goals of revenue growth, Customer delight, improved operational efficiency, and regulatory compliance. Some of the key emerging technologies being explored include Artificial Intelligence, Cloud Computing, Blockchain, and Quantum computing. One of the areas being explored using Artificial Intelligence are Conversational Agents for superior Customer experience and improving operational efficiency.
2020-06-10 12:55:06 Read the full story…
Weighted Interest Score: 3.6316, Raw Interest Score: 1.8702,
Positive Sentiment: 0.4173, Negative Sentiment 0.1236

Effort to Fund National Research Cloud for AI Advances

A bipartisan group of legislators in the US House and Senate proposed a bill in the first week of June that would direct the federal government to develop a national cloud computing infrastructure for AI research. This idea originated with a proposal from Stanford University in 2019.

The legislation was introduced by Sens. Rob Portman, R-Ohio, and Martin Heinrich, D-NM, is called the National Cloud Computing Task Force Act. It would convene a mix of technical experts across academia, industry and government, to plan for how the US should build, deploy, govern and maintain a national research cloud for AI. “With China focused on toppling the United States’ leadership in AI, we need to redouble our efforts with a sustained commitment to the best and brightest by developing a national research cloud to ensure our technical researchers get the tools they need to succeed,” stated Portman, according to an account in Nextgov. “By democratizing access to computing power we ensure that any American with computer science talent can pursue their good ideas.”

2020-06-11 21:30:41+00:00 Read the full story…
Weighted Interest Score: 3.6036, Raw Interest Score: 1.7900,
Positive Sentiment: 0.2059, Negative Sentiment 0.1267

C-Suite Banking Execs Believe AI Will Set Winning Institutions Apart

Increasing investment in AI and cloud computing will usher in new opportunities for consumers and businesses, says Temenos/Economist research. Another finding: North American financial institutions are catching up on the cloud front as regulators’ attitudes warm up.

More than three quarters (77%) of senior banking executives in a worldwide survey believe artificial intelligence capabilities will increasingly spell the difference between success and being an also-ran. Of that total figure, 32% strongly agreed with the statement: “Unlocking value from AI will be the key differentiator between winning and losing banks.” 46% agreed with the statement. Belief in AI’s potential was even stronger among North American institutions taking part in the study with 87% in agreement overall and 38% strongly agreeing.
2020-06-09 13:51:29+00:00 Read the full story…
Weighted Interest Score: 3.2365, Raw Interest Score: 1.4582,
Positive Sentiment: 0.1894, Negative Sentiment 0.1894

Game-Changing Technologies in the Data Environment of 2020

AI and machine learning were cited by several industry leaders as the most important technologies shaping today’s data environments. “We’re starting to see more success in specific use cases of machine learning, such as anomaly detection with system events, natural language processing, entity extraction, and classification technologies,” said Ranga Rajagopalan, vice president of product management for Commvault.

AI is critical to competing in the emerging economy, as it “makes it possible to go beyond what the human eye can detect and focus on a range of bad behaviors,” said David Ngo, vice president of product and engineering at Metallic. “It helps predict, identify, address, and solve our data needs.”
2020-06-10 00:00:00 Read the full story…
Weighted Interest Score: 3.2142, Raw Interest Score: 1.7033,
Positive Sentiment: 0.2241, Negative Sentiment 0.2689

Australia joins world-first AI group to tackle ethics, commercialisation

Australia will join forces with 11 other countries and the European Union to form the world’s first multilateral forum dedicated to fostering responsible development and innovation in artificial intelligence.

The forum, known as Global Partnership on Artificial Intelligence (GPAI), will seek to tackle issues such as the use of artificial intelligence in policing and surveillance, which came to a head last week when IBM, Amazon and Microsoft all said they would delay or cease work on the technology in light of the Black lives Matter protests in the US.

Speaking to The Australian Financial Review, Professor Huntington said the group would not only aim to prevent the irresponsible use of AI, but further its use for social good.

2020-06-14 00:00:00 Read the full story…
Weighted Interest Score: 3.1104, Raw Interest Score: 1.4014,
Positive Sentiment: 0.2180, Negative Sentiment 0.0623

DATA SCIENCE IN PRACTICE: FIVE COMMON APPLICATIONS (Whitepaper behind registration wall)

Learn how real companies use data science to exponentially improve products and day-to-day operations
See five concrete examples — with real use cases — of how your company can use data science in ways that won’t just help your business, but will also thrill your data scientists
See how the data science life cycle works and how you can more effectively get models into production so they can start making waves

Data science is a complicated discipline, but that doesn’t mean non-data scientists can’t understand the magic and, more importantly, the value behind the science. Walk away clearly knowing how to use data science to optimize processes and improve functions across the business — leading to more promotions and fist bumps along the way.

2020-06-10 00:00:00 Read the full story…
Weighted Interest Score: 3.0612, Raw Interest Score: 1.6582,
Positive Sentiment: 0.3827, Negative Sentiment 0.1276

Gaining Insight into the New World of Database Technologies at Data Summit Connect 2020

Database technologies are constantly changing and adding new options for enterprises. To take advantage of the new world of database technologies, enterprise and database managers need to be open to new possibilities.

Thomas Cook, director of sales, Cambridge Semantics, then offered a primer on graph database technology and the rapid growth of knowledge graphs, in a presentation titled, “AnzoGraph DB: Driving AI and Machine Insights with Knowledge Graphs in a Connected World.” Knowledge graphs are undergoing rapid adoption because they have the advantages of linking and analyzing vast amounts of interconnected data. The promise of graph technology has been there for a decade. However, the scale, performance, and analytics capabilities of AnzoGraph DB, a graph database, is a key catalyst for knowledge graph adoption, Cook said. Setting the stage for why knowledge graphs are coming to the fore, Cook cited current trends, including increasing data volumes, demand for AI and machine learning, and the increasingly complex data ecosystem.
2020-06-10 00:00:00 Read the full story…
Weighted Interest Score: 3.0587, Raw Interest Score: 1.6678,
Positive Sentiment: 0.2513, Negative Sentiment 0.0457

Revolutionizing Data Collaboration with Federated Machine Learning

We now live in a world that’s becoming more data-driven every day. Organizations across a wide range of industries are using artificial intelligence (AI) and machine learning (ML) technologies to tap into complex data sets, unearth valuable insights and drive innovation. From healthcare and government to the financial sector and beyond, advanced data science models and big data projects are unlocking insights that can deliver everything from novel approaches to preventing and treating disease to highly effective financial fraud detection and more.

But these projects aren’t without their challenges. Organizations looking to embark on data collaboration initiatives must overcome obstacles such as data ownership issues, compliance requirements for a variety of regulations and more. In today’s data-filled world, ensuring privacy and security is paramount, and the measures to which organizations must go to achieve this can make collaborative data science difficult. The potential consequences of sustaining any kind of privacy or security breach (noncompliance, fines, reputational damage, etc.) can cause organizations to shy away from sharing data sets that could spark the next life-saving medical treatment or momentous public service program.

2020-06-12 00:00:00 Read the full story…
Weighted Interest Score: 2.9271, Raw Interest Score: 1.6560,
Positive Sentiment: 0.4095, Negative Sentiment 0.3917

Data Summit Connect 2020 Presents an Introduction to Knowledge Graphs Pre-Conference Workshop

Data Summit Connect 2020 launched Monday with a full day of pre-conference workshops, followed by a free 3-day series of data-focused webinars. As part of the virtual conference hosted by DBTA and Big Data Quarterly, Joe Hilger and Sara Nash presented the first workshop, titled “Introduction to Knowledge Graphs.”

Hilger, who is COO and co-founder of Enterprise Knowledge, LLC, and Nash, who is a technical analyst with the consultancy, covered what a knowledge graph is, how it is implemented, and how it can be used to increase the value of data.

Knowledge graphs are becoming an increasingly important tool that organizations are using to manage the vast amounts of data they collect, store, and analyze. An enterprise knowledge graph’s representation of an organization’s content and data creates a model that integrates structured and unstructured data, and leverages semantic and intelligent qualities to make them “smart.”

2020-06-08 00:00:00 Read the full story…
Weighted Interest Score: 2.8755, Raw Interest Score: 1.4515,
Positive Sentiment: 0.1643, Negative Sentiment 0.1232

Researchers propose framework to measure AI’s social and environmental impact

In a newly published paper on the preprint server Arxiv.org, researchers at the Montreal AI Ethics Institute, McGill University, Carnegie Mellon, and Microsoft propose a four-pillar framework called SECure designed to quantify the environmental and social impact of AI. Through techniques like compute-efficient machine learning, federated learning, and data sovereignty, the coauthors assert scientists and practitioners have the power to cut contributions to the carbon footprint while restoring trust in historically opaque systems.

2020-06-12 00:00:00 Read the full story…
Weighted Interest Score: 2.8533, Raw Interest Score: 1.3323,
Positive Sentiment: 0.3089, Negative Sentiment 0.1738

Expanding Your Data Science and Machine Learning Capabilities (Webinar – behind registration wall)

Surviving and thriving with data science and machine learning means not only having the right platforms, tools and skills, but identifying use cases and implementing processes that can deliver repeatable, scalable business value. The challenges are numerous, from selecting data sets and data platforms, to architecting and optimizing data pipelines, and model training and deployment. In responses, new solutions have emerged to deliver key capabilities in areas including visualization, self-service and real-time analytics. Along with the rise of DataOps, greater collaboration and automation have been identified as key success factors.
2020-06-25 00:00:00 Read the full story…
Weighted Interest Score: 2.8463, Raw Interest Score: 1.8130,
Positive Sentiment: 0.2863, Negative Sentiment 0.0954

Charting Your Course to Cloud Analytics Success (Webinar – behind registration wall)

The cloud is increasingly becoming the go-to destination for data analytics at enterprises today. Looking to capitalize on the promise of reduced costs and greater scalability and flexibility, more and more organizations are adopting hybrid and multicloud strategies to break down data silos, increase collaboration and equip decision-makers with faster access to actionable business insights. However, success means creating a data management strategy and supporting it with technologies that enable you to easily span multiple clouds to ensure flexible data connectivity and access, eliminate single points of failure and maintain acceptable performance and security.

2020-06-16 00:00:00 Read the full story…
Weighted Interest Score: 2.8116, Raw Interest Score: 1.4438,
Positive Sentiment: 0.6839, Negative Sentiment 0.1520

DBTA 100 2020: The Companies That Matter Most in Data

Today, there is a constantly evolving list of data management issues that organizations are contending with. In addition to pressures of exploding data volumes, there is urgent demand for real-time, data-driven insights as well as more widespread data access. Expanding regulatory mandates also demand greater data quality and governance, as do cybersecurity threats.

The myriad, and sometimes conflicting, requirements facing data managers were highlighted in a 2020 survey report released by Unisphere Research, a division of Information Today, Inc., and sponsored by Dell EMC (“2020 Quest-IOUG Database Priorities Survey”). For roughly two out of three data managers, mundane, administrative tasks consume a substantial part of their budgets. According to Unisphere analyst Joe McKendrick, maintaining system stability—patching, fixing, upgrading—is considered by 66% of respondents to be the costliest part of their jobs. In addition, 61% indicated that much of their budget goes to maintaining uptime and availability. For 49%, security consumes sizable portions of their time.

2020-06-10 00:00:00 Read the full story…
Weighted Interest Score: 2.6779, Raw Interest Score: 1.6080,
Positive Sentiment: 0.2297, Negative Sentiment 0.3063

Tech vets lead Panda AI, a secretive new spinout from the Allen Institute for Artificial Intelligence

Veteran tech execs are heading up a stealthy new startup formed inside the Allen Institute for Artificial Intelligence (AI2) in Seattle.

Panda AI isn’t yet sharing many details about its technology or products. The company is led by CEO Aaron Goldfeder, who previously co-founded EnergySavvy, an enterprise analytics company acquired by Uplight in 2019. His co-founder and CTO is Yue Ning, a former software engineer at Amazon, LinkedIn, Twitter, and Qualtrics, where he led teams focused on text analytics. Ning also previously founded Seattle-based natural language processing startup Civet AI.

Both Goldfeder and Ning joined AI2 as entrepreneur-in-residences late last year. Here’s how Goldfeder described Panda in an email to GeekWire: “PANDA is a stealth mode B2B startup enabled by very recent breakthroughs in AI. Our goal is to help teams win more, work less, and be happier. PANDA is a next generation solution for problems that many millions of knowledge workers experience daily. Our approach was born out of personal frustration with the current generation toolset available to us while running EnergySavvy. Early adopters love PANDA and we are having a blast building it.”

2020-06-12 15:21:00+00:00 Read the full story…
Weighted Interest Score: 2.6396, Raw Interest Score: 1.4089,
Positive Sentiment: 0.2684, Negative Sentiment 0.1342

IIT Madras Is Offering Stipend Up To ₹60,000 For Fellowship In AI Research

IIT Madras through its Robert Bosch Centre for Data Science and Artificial Intelligence (RBCDSAI) offers a Post-Baccalaureate Fellowship Program to aspirants who are interested in research.

Founded in 2017, the idea of Post-Baccalaureate Fellowship Program is to provide facilities for AI research to graduates to blaze a trail in the cutting-edge technologies. However, the fellowship is only for aspirants who have been graduated within the last two years. On selection, one can be involved in the research internship for up to two years. The stipend varies for the research internship but is between ₹40,000 to ₹60,000 per month.

To apply, aspirants would be required to submit their CV, short research proposal (300-500 words), list of interesting research areas (keywords), relevant courses completed (Coursera, NPTEL, or others), and a research proposal (300-500 words).

2020-06-09 14:10:21+00:00 Read the full story…
Weighted Interest Score: 2.5917, Raw Interest Score: 1.4188,
Positive Sentiment: 0.0978, Negative Sentiment 0.0000

AWS Upgrades SageMaker Labeling Tool

Amazon Web Services has added a 3D visualization capability to its SageMaker data labeling tool used to build training data sets for machine learning models.

AWS said this week its SageMaker data labeling service called Ground Truth introduced in 2018 now includes a workflow for labeling of point clouds, a set of data points generated by tools like 3D scanners or Lidar sensors. Among the applications is labeling huge 3D data sets used to train models incorporated into self-driving car navigation systems. Those data sets can grow to hundreds of megabytes, making labeling extremely arduous. The new 3D point cloud labeling tool is billed as a custom workflow that includes a built-in editor and new “assistive” labeling features.
2020-06-10 00:00:00 Read the full story…
Weighted Interest Score: 2.5698, Raw Interest Score: 1.5096,
Positive Sentiment: 0.0000, Negative Sentiment 0.0368

Modern Data Warehousing: Enterprise Must-Haves (Webinar – registration wall)

To fit into modern analytics ecosystems, legacy data warehouses must evolve – both architecturally and technologically – to deliver the agility, scalability and flexibility that business need to thrive in today’s data-driven economy. Alongside new architectural approaches, a variety of technologies have emerged as key ingredients of modern data warehousing, from data virtualization and cloud services, to Hadoop and Spark, and machine learning and automation. To educate IT decision makers and data warehousing professionals about the must-have capabilities for modern data warehousing today – how they work and how best to use them – DBTA is hosting a special roundtable webinar on November 19th.

2020-11-19 00:00:00 Read the full story…
Weighted Interest Score: 2.5448, Raw Interest Score: 1.6053,
Positive Sentiment: 0.0944, Negative Sentiment 0.0000

6 Important Big Data Future Trends, According To Experts

These big data future trends as predicted by experts are key to watch for in the coming future. Here’s what to expect down the line.

Many people agree that big data is here to stay and not a mere fad. Something that is not so clear-cut to everyday individuals concerns the future trends of big data analytics. These technologies are quickly evolving. What does that mean for the businesses that use them now or will soon?

What is big data in simple terms? It encompasses both the structured and unstructured information kept by an entity that is collectively too large for traditional systems and techniques to process. It also relates to the speed of the processing capability. Some businesses need insights in virtually real-time, and big data software can provide them, whereas traditional methods could not.

Understanding what’s ahead for big data technologies and use cases is more straightforward if people tune in to what experts have to say. Here are some glimpses into what’s possible, based on their perceptions.

2020-06-09 09:05:00+00:00 Read the full story…
Weighted Interest Score: 2.5419, Raw Interest Score: 1.5291,
Positive Sentiment: 0.1133, Negative Sentiment 0.0566

Kerala-Based Enterprise Automation Startup Raises $18 Million Funding

Kerala-based enterprise automation startup Jiffy.ai has announced that the company has raised $18 million in their Series A funding led by Nexus Venture Partners. This funding has been done in participation with Rebright Partners and W250 Venture Fund for developing products as well as expanding into newer markets around the world.

Run by Malayali entrepreneur and a former president of Thiruvananthapuram-based IT company ‘Envestnet’ Babu Vinod Sivasdasan, Jiffy.ai, a brand of Paanini Inc, is a company that uses technologies like RPA, machine learning and artificial intelligence to help businesses in automating their tasks and processes that are usually performed with manual intervention. Their solutions are designed to make their customers’ operations more time and cost-efficient. Alongside, the platform also includes a design studio for no-code application development, and a configurable analytics dashboard to monitor automated processes.

2020-06-15 06:12:53+00:00 Read the full story…
Weighted Interest Score: 2.4951, Raw Interest Score: 1.4921,
Positive Sentiment: 0.0649, Negative Sentiment 0.0973

A/B Testing Machine Learning Models in Production Using Amazon SageMaker (Discussion)

A/B Testing Machine Learning Models in Production Using Amazon SageMaker (Discussion)

Thinking about A/B Testing ML Models in Production with a Potential Real-time Inference ML Workflow

Kieran Kavanaugh, David Nigenda, and I, recently wrote a post for the AWS Machine Learning Blog about A/B Testing ML models in production using Amazon SageMaker. I recommend reading the post, and also checking out our accompanying Jupyter notebook (A/B Testing with Amazon Sagemaker).
2020-06-15 00:44:24.918000+00:00 Read the full story…
Weighted Interest Score: 2.4727, Raw Interest Score: 1.9042,
Positive Sentiment: 0.0577, Negative Sentiment 0.1154

Can Australia become known for safe and ethical AI?

Liesl Yearsley and Hanno Blankenstein have more in common than merely being overseas-born founders of promising Australian technology start-ups.

Both of them have founded companies that use artificial intelligence (AI) to help create an edge for their customers. Yearsley’s company, Akin, is using AI to create bots that can converse with humans in a lifelike way. Blankenstein’s company, Unleash Live, uses AI for real-time analysis of video footage.

Such AI-based surveillance systems, says Toby Walsh, a professor of artificial intelligence at the University of NSW, are now at risk of becoming “toxic assets” for the companies that develop and sell them, to the point where many companies will be forced to abandon the technology altogether.

“Face recognition, along with other misuses of surveillance, is going to be a topic that will trouble us increasingly. These won’t be the last tech companies that decide to get out, and rightly so…
2020-06-14 00:00:00 Read the full story…
Weighted Interest Score: 2.4091, Raw Interest Score: 0.8530,
Positive Sentiment: 0.2095, Negative Sentiment 0.3143

What To Expect When You Start A Job As A Junior Data Scientist?

If you are beginning your career as a data scientist, this article could give you a general idea of what to expect from the first job. There are factors to consider when starting a data science job, like whether a company uses machine learning or business analytics; the kind of tools the company uses for data science/analytics, or finally whether it is a large company or a small startup. Such variables are important in making the initial decisions on what to learn and where to focus on to advance your career.

When beginning a fresh job, whether you are a new hire or an experienced professional, try to lower your expectations initially and then slowly adjust to the work pace and processes. But this is true for any job role in any sector, and not specific to data science, or IT. As a junior data scientist, you may have stronger skills in statistics than in programming, and therefore, you may need a lot of learning in the first few years of your career. Even if you understand machine learning statistics and Python, you may still need to expand your knowledge in tools and libraries such as containers, PyTorch, Keras and further improve programming.
2020-06-15 10:30:00+00:00 Read the full story…
Weighted Interest Score: 2.3724, Raw Interest Score: 1.3679,
Positive Sentiment: 0.1680, Negative Sentiment 0.1920

How to Use Angular To Deploy TensorFlow Web Apps

Using Python-built models in Angular-built web apps

In the new age of machine learning and AI, Python is undoubtedly the go-to language for any budding engineer. Clean, pseudocode looking syntax — and the biggest scientific computing and machine learning communities in the world have produced the perfect language for developers looking to make their machines a little smarter. However, deploying anything built with Python to the masses is not easy.
2020-06-14 13:11:54.205000+00:00 Read the full story…
Weighted Interest Score: 2.1918, Raw Interest Score: 1.3049,
Positive Sentiment: 0.3434, Negative Sentiment 0.0000

U.S. Special Ops Launches $600M Analytics Effort

U.S. Special Operations Command plans to field a “global analytics platform” that would add data science and machine learning tools to intelligence analysts’ workflow while running on upgraded micro-services infrastructure.

Special Ops Command released a contract notice on June 5 seeking suppliers for the analytics platform. According to the notice, the analytics contract could be worth as much as $600 million over the next decade.
2020-06-09 00:00:00 Read the full story…
Weighted Interest Score: 2.1292, Raw Interest Score: 1.3226,
Positive Sentiment: 0.1470, Negative Sentiment 0.2204

Dream Forward Acquired as Retirement Plan Tech Consolidation Heats Up

In light of the greater importance being placed on scalable communication technology, and in the face of recent pandemic-related lockdowns, 401(k) provider and chatbot provider Dream Forward is being acquired by Expand Financial.

Best known for its technology licensing business, particularly its white-labeled chatbot, Dream Forward entered the 401(k) business four years ago. Its tech, which is used by record-keepers and retirement plan–focused financial advisors, “can explain all of the nuances of saving for retirement and 401(k)/403(b) rules and regulations,” Kahn said.

2020-06-11 19:58:53+00:00 Read the full story…
Weighted Interest Score: 2.0914, Raw Interest Score: 0.9737,
Positive Sentiment: 0.6329, Negative Sentiment 0.3408

Leverage the Power of Data as a Strategic Currency (Registration Wall)

Now more than ever, data is crucial to informing critical decisions and business growth. However, without analytics, data is just noise. But with analytics, data becomes insight. Learn how a modernized data system powered by AI, machine learning, and cloud-enabled architecture from Insight and Microsoft Azure Synapse can amplify the power of data as strategic currency by providing analytics at blazing speeds and significantly lower costs.
2020-06-09 00:00:00 Read the full story…
Weighted Interest Score: 5.5928, Raw Interest Score: 3.3937,
Positive Sentiment: 0.2262, Negative Sentiment 0.4525


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