Industry News

Industry News

AI and ML News Nov 2020 : HBO ‘Superintelligence’ in Seattle : AI researchers movie reality check : Does Ethical AI = Trustworthy AI : Should Companies Pay Users For Their Data? : Googles ML Rainforest Gunshot Recognition : Machine Learning session at re:Invent 2020 : S&P Global & IHS Markit To Merge

AI & Machine Learning News. 30, November 2020

AI & Machine Learning News. 30, November 2020

AI and Machine Learning Newsletter

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?

‘Superintelligence’ in Seattle: AI researchers do a reality check on a movie that adds romance to tech

Seattle, Microsoft and the field of artificial intelligence come in for their share of the spotlight in “Superintelligence” — an HBO Max movie starring Melissa McCarthy as the rom-com heroine, and comedian James Corden as the world’s new disembodied AI overlord.

But how much substance is there behind the spotlight? Although the action is set in Seattle, much of the principal filming was actually done in Georgia. And the scientific basis of the plot — which involves an AI trying to decide whether or not to destroy the planet — is, shall we say, debatable.
Fortunately, we have the perfect team to put “Superintelligence” to the test, as a set-in-Seattle movie as well as a guide to the capabilities of artificial intelligence.
2020-11-26 18:00:00+00:00 Read the full story…
Weighted Interest Score: 2.0458, Raw Interest Score: 0.7833,
Positive Sentiment: 0.0490, Negative Sentiment 0.1567

CloudQuant Thoughts : A new movie about AI, no matter how silly it is, how could I not put it’s trailer at the top?

Ethical AI isn’t the same as trustworthy AI, and that matters

Artificial intelligence (AI) solutions are facing increased scrutiny due to their aptitude for amplifying both good and bad decisions. More specifically, for their propensity to expose and heighten existing societal biases and inequalities. It is only right, then, that discussions of ethics are taking center stage as AI adoption increases.
In lockstep with ethics comes the topic of trust. Ethics are the guiding rules for the decisions we make and actions we take. These rules of conduct reflect our core beliefs about what is right and fair. Trust, on the other hand, reflects our belief that another person — or company — is reliable, has integrity and will behave in the manner we expect. Ethics and trust are discrete, but often mutually reinforcing, concepts.
So is an ethical AI solution inherently trustworthy?
2020-11-28 00:00:00 Read the full story…
Weighted Interest Score: 3.2303, Raw Interest Score: 0.9299,
Positive Sentiment: 0.1894, Negative Sentiment 0.3272

CloudQuant Thoughts : I would imagine, and the article seems to back this up, that once we get past the initial problems with AI/ML (where human biases unduly influence the machines decisions), most peoples problems will not be with the AI but how it is used. I bristle whenever I see an insurance advert for using monitoring devices to “lower” car insurance. Why would an insurance company behave any more ethically with AI guidance.

Should Companies Pay Users For Their Data?

Every day, humans produce an astonishing amount of data, to the tune of about 2.5 quintillion bytes. Google processes 40,000 searches a second, 1.4 billion people login into Facebook every day. Every minute, 16 million text messages are sent out, 156 million emails are shared, and 600 new pages are created in Wikipedia. Needless to say that the amount of data generated every day, including this very moment is astronomical. However, the companies and enterprises, who ultimately benefit from this data, are not complaining; in fact, they are glad.
Data is the cog in the wheel of modern society. Given how data is an essential driving factor, should not the users who provide this data in the first place, be paid for use by the tech companies? This question has gained prominence over the recent years, with even experts and influential figures are raising.

2020-11-30 09:30:23+00:00 Read the full story…
Weighted Interest Score: 1.9592, Raw Interest Score: 1.1462,
Positive Sentiment: 0.2159, Negative Sentiment 0.2159

CloudQuant Thoughts : Yes, Yes and thrice Yes. My data is my data. Also, most people would be happy to give up more data for better services. Don’t call me to ask me if I want new windows… wait until I add a desire for new windows to my data vault.

Google Enables Machine Learning for Gunshot Recognition in the Rainforest

The World Wildlife Fund (WWF) estimates that poaching is the root of a $20 billion-a-year industry. This thriving, illegal practice is largely possible because enforcement of poaching laws is so difficult: personnel must monitor enormous swaths of land for a handful of rare animals and sneaky humans – and they must identify infractions quickly enough to make a meaningful difference. For the past three years, the Zoological Society of London (ZSL) has been partnered with Google Cloud to use machine learning to streamline these processes and protect endangered species. Now, ZSL and Google Cloud are highlighting a new tool on that front: acoustic data monitoring.
“The analysis of acoustic (sound) data to support wildlife conservation is one of the major lines of work at ZSL’s monitoring and technology programme,” wrote Omer Mahmood, a head of customer engineering for Google Cloud, UK and Ireland. “Compared to camera traps that are limited to detection at close range, acoustic sensors can detect events up to 1 kilometre (about half a mile) away. This has the potential to enable conservationists to track wildlife behaviour and threats over much greater areas.”
2020-11-24 00:00:00 Read the full story…
Weighted Interest Score: 1.9690, Raw Interest Score: 1.1656,
Positive Sentiment: 0.1494, Negative Sentiment 0.2391

CloudQuant Thoughts : Cannot help but think this is a solution they had to hand (ie that just happened to get a nice greenwash!

DeepMind solves 50-year-old ‘grand challenge’ with protein folding A.I.

  • DeepMind has developed a piece of AI software called “AlphaFold” that can accurately predict the structure that proteins will fold into in a matter of days.
  • Predicting the shape that a protein will fold into is important because it determines their function and nearly all diseases, including cancer and dementia, are related to how proteins function.
  • “DeepMind has jumped ahead,” said Professor John Moult, who is the chair of a group called CASP (Critical Assessment for Structure Prediction).

Alphabet-owned DeepMind has developed a piece of artificial intelligence software that can accurately predict the structure that proteins will fold into in a matter of days, solving a 50-year-old “grand challenge” that could pave the way for better understanding of diseases and drug discovery.
Every living cell has thousands of different proteins inside that keep it alive and well. Predicting the shape that a protein will fold into is important because it determines their function and nearly all diseases, including cancer and dementia, are related to how proteins function.

2020-11-30 00:00:00 Read the full story…
Weighted Interest Score: 1.9317, Raw Interest Score: 1.0608,
Positive Sentiment: 0.3536, Negative Sentiment 0.2593

CloudQuant Thoughts : 50 year grand challege… Pfft…Tick.. NEXT!!!

A developer’s guide to re:Invent 2020 machine learning sessions – STARTS TODAY

This year will be remembered for many reasons. It has been a year of big changes in our lives and habits and a time when we found new ways to do the things we love. 2020 will be remembered as the first year without Amazonians from everywhere gathering in Vegas for the traditional re Invent.
Luckily, it won’t be a year without re:Invent because AWS decided to shift the conference completely online, with a catalog of almost 2000 unique sessions ranging from IoT to machine learning applications to infrastructure and serverless.
As a developer and a machine learning practitioner, digging into such a vast list to extract the best sessions to watch while doing our everyday job is not easy. Here this article comes in handy, trying to enucleate the talks you can’t miss, grouped into four main themes, with a bit of context to support deep diving into each topic.

2020-11-30 14:14:23.359000+00:00 Read the full story…
Weighted Interest Score: 2.3385, Raw Interest Score: 1.4198,
Positive Sentiment: 0.3395, Negative Sentiment 0.0926

CloudQuant Thoughts : 2000 unique sessions sounds amazing but unmanageable. Unsurprisingly, as an Amazon event it Amazon appear to be the powerhouse, from Amazon Comprehend to Amazon Personalize they have their fingers in so many pies!

S&P Global And IHS Markit To Merge

All-Stock Transaction Valuing IHS Markit at $44 Billion, Powering the Markets of the Future

Joins Two World-Class Organizations with Unique, Highly Complementary Assets to Enhance Customer Value Proposition

Combined Company to Benefit from Increased Scale and Mix Across Core Markets with Attractive Growth Adjacencies

2020-11-30 07:07:33+00:00 Read the full story…
Weighted Interest Score: 2.9668, Raw Interest Score: 1.6110,
Positive Sentiment: 0.4658, Negative Sentiment 0.1359

CloudQuant Thoughts : Two major players in the Alternative Data business coming together. S&P is expanding its data empire.

Researchers Release An AI-Based Research Paper Summariser

In an attempt to simplify the process of summarising complex scientific research papers, researchers at the Allen Institute for Artificial Intelligence have released a new AI-based tool that summarises the text from scientific papers and present it in a few sentences.
Considering scientific research papers are complex to understand because of the language it is presented in, it becomes a challenge for many who are willing to work on the same or trying to be updated with scientific literature. And, that is why the researchers from Allen Institute for Artificial Intelligence came out with this new AI-based model — Semantic Scholar — that automatically generates a single-sentence summary using GPT-3 style techniques. This helps in locating the right paper and deciding whether to dedicate time to read that complex paper or not, stated by the official website.

2020-11-24 12:35:25+00:00 Read the full story…
Weighted Interest Score: 3.0324, Raw Interest Score: 1.9054,
Positive Sentiment: 0.1411, Negative Sentiment 0.0353

The U.S. government needs to get involved in the A.I. race against China, Nasdaq executive says

The U.S. needs to take a “strategic approach” as it competes with China on artificial intelligence, according to a Nasdaq executive.

AI is an area that is going to only develop in partnership with government, and U.S. authorities need to get involved, said Edward Knight, vice chairman of Nasdaq.

The Chinese government has already started “investing heavily” and working with their private sector to develop new technologies based on artificial intelligence, he said.

Beijing in 2017 said it wa…
2020-11-25 00:00:00 Read the full story…
Weighted Interest Score: 5.0746, Raw Interest Score: 1.8905,
Positive Sentiment: 0.0995, Negative Sentiment 0.0000

Research provision hits digital inflection point

Research providers must have been wondering what else could be thrown at them as they entered 2020, given the upheaval that the unbundling of execution and research has brought to their industry during the past few years. The answer was Covid and the switch to remote delivery.
They still face the challenge of making sure they provide quality research at the right valuation, as budgets are capped, but now also have to do this with the shift to digital distribution. Establishing a clear valuation and pricing mechanism for research provision remains a challenge for asset managers, even as they expand their consumption of different types of analysis.The demand for research in areas such as ESG and artificial intelligence (AI) has seen an acceleration in the use of expert networks and greater discussion over what should be deemed a research expense and what should come from the market data budget.

2020-11-23 12:29:36.409000 Read the full story…
Weighted Interest Score: 2.9026, Raw Interest Score: 1.5933,
Positive Sentiment: 0.1874, Negative Sentiment 0.2812

Liquidnet Adds Senior Data Science Staff to IA Unit • Integrity Research

Liquidnet, a New York-based technology driven trading and analytics provider, recently announced that it has added three new senior staff to its Investment Analytics (IA) data science team, and appointed former Prattle executive Steven Nichols as Head of NLP and Unstructured Data for the firm.
Liquidnet recently promoted Steven Nichols to the role of Head of NLP and Unstructured Data. Nichols joined Liquidnet through the firm’s 2019 acquisition of technology company Prattle, where he was a Director of Data Science. Steven has been instrumental in the development of Liquidnet’s NLP capabilities and their integration into the Liquidnet Investment Analytics product suite, combining AI tools like machine learning and NLP with traditional and alternative data to help uncover the actionable insights hidden within data and content. In his new role, Steven will guide the strategic direction of the NLP team while serving as one of the leaders on the Liquidnet data science team.

2020-11-23 07:30:00+00:00 Read the full story…
Weighted Interest Score: 4.9827, Raw Interest Score: 2.0127,
Positive Sentiment: 0.1059, Negative Sentiment 0.0794

TensorFlow 2 on Raspberry Pi. TensorFlow Lite on Raspberry Pi 4 can…

With the new Raspberry Pi 400 shipping worldwide, you might be wondering: can this little powerhouse board be used for Machine Learning?

The answer is, yes! TensorFlow Lite on Raspberry Pi 4 can achieve performance comparable to NVIDIA’s Jetson Nano at a fraction of the dollar and power cost. You achieve real-time performance with state-of-the-art neural network architectures like MobileNetV2 by adding a Coral Edge TPU USB Accelerator.

2020-11-22 19:13:30.090000+00:00 Read the full story…
Weighted Interest Score: 4.8173, Raw Interest Score: 1.5105,
Positive Sentiment: 0.1092, Negative Sentiment 0.0546

AI Weekly: The state of machine learning in 2020

It’s hard to believe, but a year in which the unprecedented seemed to happen every day is just weeks from being over. In AI circles, the end of the calendar year means the rollout of annual reports aimed at defining progress, impact, and areas for improvement.

The AI Index is due out in the coming weeks, as is CB Insights’ assessment of global AI startup activity, but two reports — both called The State of AI — have already been released.

Last week, McKinsey released its global survey on the state of AI, a report now in its third year. Interviews with executives and a survey of business respondents found a potential widening of the gap between businesses that apply AI and those that do not.

2020-11-27 00:00:00 Read the full story…
Weighted Interest Score: 4.3369, Raw Interest Score: 1.6162,
Positive Sentiment: 0.1515, Negative Sentiment 0.1515

How software engineers and data scientists can collaborate together

Data scientists are great mathematicians with a lot of cross-disciplinary knowledge and a super ability for analysis. The task of this specialist is to find the ideal formula for training artificial intelligence. Among all the existing algorithms, they should look for the one that is better suited to solving the project’s problems and understand what exactly is going wrong. However, in order to increase the competitive advantage of the company, data scientists need to cooperate with software engineers, like dedicated Laravel engineer
Working with data is more research-oriented than software development, for instance, Laravel application development. Laravel developer can take over the technical side of the issue. At any stage of the work, both data scientists and engineers must feel responsible for the problem and be able to contribute. There is continuous communication, so that potential inconsistencies are identified early. In this article, we’ll take a closer look at the challenges a software developer and data scientist face in the process and how collaboration between them can be improved.
2020-11-24 12:36:23+00:00 Read the full story…
Weighted Interest Score: 4.2899, Raw Interest Score: 2.3059,
Positive Sentiment: 0.3334, Negative Sentiment 0.3751

Humans can’t escape accountability for decisions made by artificial intelligence

Algorithms are in theory a useful tool, but their “black box” decisions shouldn’t be accepted without question
The way organisations make decisions is changing. An explosion in the volume of data, coupled with the growing sophistication and accessibility of algorithms, means that increasingly organisations have opportunities to use machine learning and artificial intelligence to support decision-making….
This requires good, anticipatory governance. Many of the high profile cases of algorithmic bias could have been anticipated with careful evaluation and mitigation of the potential risks. Organisations, and more specifically their leaders, need to make sure that the right capabilities and structures are in place to ensure that this happens both before algorithms are introduced into decision-making processes, and throughout the process. Doing this …
2020-11-27 00:00:00 Read the full story…
Weighted Interest Score: 4.0816, Raw Interest Score: 1.1946,
Positive Sentiment: 0.2987, Negative Sentiment 0.1991

AI in Finance: how to finally start to believe your backtests [3/3]

Understanding strategy risk and the probability of overfitting: small numbers that change everything

Note from Towards Data Science’s editors: While we allow independent authors to publish articles in accordance with our rules and guidelines, we do not endorse each author’s contribution. You should not rely on an author’s works without seeking professional advice. See our Reader Terms for details.

This is the third and the last article in a short series about “how to believe the backtests”. We have started with an overview o…
2020-11-30 14:06:55.841000+00:00 Read the full story…
Weighted Interest Score: 4.0279, Raw Interest Score: 2.1446,
Positive Sentiment: 0.2437, Negative Sentiment 0.2275

ON THE MOVE: Khan Joins Nasdaq’s Quandl

Nasdaq’s Quandl, an alternative data provider, appointed Hamza Khan as Head of European Data. Khan was formerly the CEO and founder of Suburbia, a technology company that specialized in alternative data solutions. Based in Amsterdam with connections across the continent on the buy and sell side, Khan will lead the organization’s data strategy in Europe and help expand its presence in the European market. Khan began his career as a quantitative analyst and was the head of commodities strategy at ING prior to founding Suburbia.

2020-11-23 04:12:22-05:00 Read the full story…
Weighted Interest Score: 3.9007, Raw Interest Score: 1.8514,
Positive Sentiment: 0.1322, Negative Sentiment 0.0000

GrAI Matter Labs Raises $14M to Bring Fastest AI per Watt to Every Device on the Edge

GrAI Matter Labs, a pioneer of brain inspired ultra-low latency computing, today announced its latest financing round of $14 million. The round was led by iBionext, joined by all existing investors and newly welcomed Bpifrance through the Future Investment Program and Celeste Management. The company will utilize the funds to accelerate design and market launch of its first GrAI® full-stack AI system-on-chip platform, to deliver on customer needs at the edge.
GrAI Matter Labs’ programmable NeuronFlow™ technology enables industry-leading inference latency efficiently – more than an order of magnitude better than competing solutions. Its current accelerator chip GrAI One and the GrAI One HDK are available for product evaluation and application programming. The upcoming GrAI® full-stack AI system-on-chip platform will drive a significant step in visual inference capabilities in robotics, industrial automation, AR/VR and surveillance products and markets.

2020-11-23 00:00:00 Read the full story…
Weighted Interest Score: 3.7072, Raw Interest Score: 1.6497,
Positive Sentiment: 0.4124, Negative Sentiment 0.0317

How Much Does The Future Depend Upon Artificial Intelligence?

AI has changed the world and it is not going to stop. It’s time for you to know what it offers.
Artificial Intelligence has grown and been adapted to become a game-changer for conducting businesses in the 21st century. From eliminating guesswork from your decision making to making repetitive and mindless tasks redundant, AI has already become a major attraction among the biggest businesses in the world. As good trickle-down effect works, the rest of the world is also going through this inevitable development.
Let’s begin and understand what makes AI the need of the hour and where it drives our future.
2020-11-23 13:50:44+00:00 Read the full story…
Weighted Interest Score: 3.5768, Raw Interest Score: 1.3609,
Positive Sentiment: 0.1864, Negative Sentiment 0.1864

3 Ways To Utilize The Power Of Artificial Intelligence For Your Marketing Today

3 Reasons You Should Consider Artificial Intelligence For Your Marketing purposes with some useful links to help you get started.
Artificial Intelligence is the main talking point of the entire century. With the advancements in Artificial Intelligence (AI) and technology, nobody wants to fall behind in their marketing strategies, especially tech giants and modern start-ups. This scenario leads to some engrossing questions in our minds.

Some of these questions are — What are the ways you could utilize this growing trend to your benefit? How can you make the best possible us…
2020-11-30 14:55:22.374000+00:00 Read the full story…
Weighted Interest Score: 3.4955, Raw Interest Score: 1.9270,
Positive Sentiment: 0.5506, Negative Sentiment 0.3441

AI rewrites 21st century debt collection practices

Artificial Intelligence (AI) technologies are the focus of intense excitement at the moment, and nowhere more so than in the fintech sector. In just the last year, we’ve looked at the rise of AI in retail banking, and also explored whether AI is the future of commercial lending. And while many of the promises of AI have yet to be realised, it’s increasingly apparent that everyone is looking to the technology to revolutionise the banking industry.
The same cannot be said for debt collection firms. To say that these firms have an image problem is to understate the negativity with which they are regarded by both consumers and a new breed of responsible fintech companies. Think of debt collection, and the immediate image that comes to mind is a string of threateningly incessant phone calls demanding money from scared debtors. Forward-thinking creditors have begun to wonder if such tactics are efficient or effective.
It doesn’t have to be like this, some debt collectors have realised, and are looking to change stereotypes via innovative uses of technology. AI is a key component of this. In this article, we’ll look at how they are doing so.
2020-11-25 00:01:01+00:00 Read the full story…
Weighted Interest Score: 3.3481, Raw Interest Score: 1.2923,
Positive Sentiment: 0.2891, Negative Sentiment 0.3741

Optimizing AI and Deep Learning Performance

As AI and deep learning uses skyrocket, organizations are finding they are running these systems on similar resource as they do with high-performance computing (HPC) systems – and wondering if this is the path to peak efficiency.
Ostensibly AI and HPC architectures have a lot in common, as AI has evolved into even more data-intensive machine learning (ML) and deep learning (DL) domains (Figure 1). Workloads often require multiple GPU systems as a cluster, and share those systems in a coordinated way among multiple data scientists. Secondly, both AI and HPC workloads require shared access to data at a high level of performance and communicate over a fast RDMA-enabled network. Especially in scientific research, the classic HPC systems nowadays tend to have GPUs added to the compute nodes to have the same cluster suitable for classic HPC and new AI/DL workloads.
Yet AI and DL are different from HPC, their applications needs are different, and the deep learning process in particular (Figure 2) has requirements that simply buying more GPU servers won’t fix.

2020-11-26 00:00:00 Read the full story…
Weighted Interest Score: 3.3331, Raw Interest Score: 1.7056,
Positive Sentiment: 0.2360, Negative Sentiment 0.1066

Bayesian probability mass estimation using TensorFlow

When all you have are categorical variables

I have spent some time studying data with categorical variables trying to explore many ways to encode them into numeric features. What if all your variables are categorical? One of the mechanism to describe this scenario known as contingency tables.

Contingency tables in their essence are (potentially multidimensional) tables where rows, columns and other dimensions represent categorical variables, and the cells contain counts of the occurrences of the combinations. As an example, consider a simple contingency table that represents salary (rows) vs. years of experience (columns). The data are taken from [1] and reported by a study conducted by the Department of Energy.
The task of probability mass estimation is to learn probabilities of every combination of categories. A naïve approach would be to set the probabilities as fractions of observed cell count and total sample size:

2020-11-23 13:46:21.146000+00:00 Read the full story…
Weighted Interest Score: 3.1882, Raw Interest Score: 1.4814,
Positive Sentiment: 0.1470, Negative Sentiment 0.1244

Why Data Republic is hitting reset

Data Republic, a company that attracted millions in funding from the likes of Qantas, NAB, Westpac and Singtel for its data marketplace platform has reset its strategy and will now offer enterprise software.

The company sees more potential in facilitating accessibility and innovation than in data commercialisation, and wants to change now to move with the evolving data economy.

2020-11-29 19:00:51+00:00 Read the full story… GONE: Closest story
Weighted Interest Score: 3.1780, Raw Interest Score: 1.8064,
Positive Sentiment: 0.2529, Negative Sentiment 0.0361

Implement Expectation-Maximization(EM) in Python from scratch

Unsupervised and Semi-supervised Gaussian Mixture Models (GMM)

When companies launch a new product, they usually want to find out the target customers. If they have data on customers’ purchasing history and shopping preferences, they can utilize it to predict what types of customers are more likely to purchase the new product. 
2020-11-27 16:27:21.505000+00:00 Read the full story…
Weighted Interest Score: 3.1173, Raw Interest Score: 1.4003,
Positive Sentiment: 0.0289, Negative Sentiment 0.1444

Industry Looks to Unlock Liquidity in Chats

Maryanne Richter, executive director, electronic credit trading strategy, global credit at Morgan Stanley, said the bank is looking for a way to automatically convert chats into request for quotes in credit markets.

Richter spoke in a webinar, Unlocking Liquidity through People and Machines, hosted by consultant Greenwich Associates last week.

2020-11-25 10:36:45-05:00 Read the full story…
Weighted Interest Score: 3.0705, Raw Interest Score: 1.5181,
Positive Sentiment: 0.2693, Negative Sentiment 0.1959

The Maturation of Data Science

Data science used to be somewhat of a mystery, more of a dark art than a repeatable, scientific process. Companies basically entrusted powerful priests called data scientists to build magical algorithms that used data to make predictions, usually to boost profits or improve customer happiness. But in recent years, the field has matured to a remarkable degree, and that is enabling progress to be made on multiple fronts, from ModelOps and reproducibility to ethics and accountability.
About five years ago, the worldwide scientific community was suffering a “reproducibility crises” that impacted a wide range of scientific endeavors, including so-called hard sciences like physics and chemistry. One of the hallmarks of the scientific method is that experiments must be reproducible and will give the same results, but that lofty goal too often was not met.
Data science was not immune to this problem, which should not be surprising, given the relative newness and the probabilistic nature of the field. And when you mix in the black box nature of deep learning models and data that reflects a rapidly changing world, sometimes it seems a miracle that an algorithm of any complexity could generate the same result at two points in time.
2020-11-25 00:00:00 Read the full story…
Weighted Interest Score: 2.8104, Raw Interest Score: 1.2893,
Positive Sentiment: 0.2904, Negative Sentiment 0.2207

Future of Business Intelligence in the Data-Driven Economy

As the use of intelligence technologies is staggering, knowing the latest trends in business intelligence is a must. The market for business intelligence services is expected to reach $33.5 billion by 2025. Here we’ve prepared a detailed outline about the future of BI, including main trends, challenges, specifics, BI-as-a-Service, and most promising BI services of today.

In this article, you’ll discover:

  • upcoming trends in business intelligence
  • what benefits will BI provide for businesses in 2020 and on?
  • top 5 key platforms that control the future of business intelligence
  • impacts BI may have on your business in the future

2020-11-24 21:05:51+00:00 Read the full story…
Weighted Interest Score: 2.7900, Raw Interest Score: 1.5959,
Positive Sentiment: 0.4622, Negative Sentiment 0.1541

Amazon Targeting Banks, Financial Firms for Fresh AWS Talent

When Amazon Web Services (AWS), Amazon’s cloud division, announced its third quarter results for 2020 a few weeks ago, it disclosed a 37 percent year-on-year increase in revenues. That revenue growth appears to have catalyzed hiring, and Amazon has a very particular target in mind when it comes to poaching top talent.
AWS currently has over 360 vacancies in New York City, mostly for solutions architects, but also for roles such as software developers and account managers (including two roles for account managers in financial services). Recent history suggests that at least some of these roles will be filled by people moving from leading investment banks.
AWS likes to hire from Goldman Sachs. One of its most recent Goldman hires is Roger Li, a former Goldman VP and member of the machine learning team in Jersey City. Li joined AWS this month as an applied scientist in NYC, and he will be in good company: In June, AWS also poached Jeff Savio, a VP in Goldman’s asset management business, to manage accounts with fintech start-ups.

2020-11-23 00:00:00 Read the full story…
Weighted Interest Score: 2.7750, Raw Interest Score: 1.7863,
Positive Sentiment: 0.1323, Negative Sentiment 0.0662

How to Go Beyond an Ordinary Data Scientist

Ways to be Distinguished in the Age of Data Science Boom

Suppose you are the hiring manager for a data scientist position, and interviewing a prospective candidate. The candidate starts to express the skills hoping they are enough for the position and the best card among these skills is MS Excel capability. What would you think about this candidate? I suppose most of you would consider this candidate as mediocre, which is ineligible for most of the companies. Let’s make a little change in our hypothetical interview by replacing MS Excel with predictive modelling. In the new scenario, the candidate obviously has more chances to be recruited and considering the high need for data science skilled people in today’s business world, it is fairly reasonable. But how long will being proficient for just machine learning be enough to stay ahead of the game? I cannot say precisely, however, I do believe the destiny of basic machine learning skills will be similar to Excel.

2020-11-30 13:41:12.081000+00:00 Read the full story…
Weighted Interest Score: 2.7581, Raw Interest Score: 1.3675,
Positive Sentiment: 0.4156, Negative Sentiment 0.2815

The Top Trends in Data Management for 2021

From the rise of hybrid and multicloud architectures, to the impact of machine learning and automation, the business of data management is constantly evolving with new technologies, strategies, challenges and opportunities. The demand for fast, wide-range access to information is growing. At the same time, the need to effectively integrate, govern, protect and analyze data is also intensifying. All the while, data environments are increasing in size and complexity — traversing relat…
2020-12-10 00:00:00 Read the full story…
Weighted Interest Score: 2.7460, Raw Interest Score: 1.7571,
Positive Sentiment: 0.0764, Negative Sentiment 0.0764

How Hasty uses automation and rapid feedback to train AI models and improve annotation

Computer vision is playing an increasingly pivotal role across industry sectors, from tracking progress on construction sites to deploying smart barcode scanning in warehouses. But training the underlying AI model to accurately identify images can be a slow, resource-intensive endeavor that isn’t guaranteed to produce results. Fledgling German startup Hasty wants to help with the promise of “next-gen” tools that expedite the entire model training process for annotating images.
The global computer vision market was pegged at $11.4 billion in 2020, a figure that is projected to rise to more than $19 billion by 2027. Data preparation and processing is one of the most time-consuming tasks in AI, accounting for around 80% of time spent on related projects. In computer vision, annotation, or labeling, is a technique used to mark and categorize images to give machines the meaning and context behind the picture, enabling them to spot similar objects. Much of this annotation work falls to trusty old humans.
2020-11-24 00:00:00 Read the full story…
Weighted Interest Score: 2.6510, Raw Interest Score: 1.4578,
Positive Sentiment: 0.1310, Negative Sentiment 0.2785

Top AI Based Smartphone Apps Of 2020

For many years, AI has eluded smartphones owing to its own resource-heavy nature. With the introduction of tools like TFLite and other frameworks, machine learning models became lighter; light enough to be boarded onto the limited space on a mobile or an edge device. This allowed app developers to incorporate models into their apps for better experience. From Maps to face filters, from e-learning to contact tracing during COVID-19, AI came in quite handy. In this article we list few of the most popular AI based apps (not necessarily created this year) that have made their presence felt this year.

  • COVID-19 Sounds App
  • Microsoft Math Solver
  • AI Dungeon

2020-11-28 04:30:00+00:00 Read the full story…
Weighted Interest Score: 2.5899, Raw Interest Score: 1.0234,
Positive Sentiment: 0.0981, Negative Sentiment 0.1542

This is how we’ll merge with AI

The relationship between humans and AI is something of a dance. We and AI come close together operating collaboratively, then are pushed away by the impossibility, only to stumble but return attracted by the potential. It is perhaps fitting that the dance community is beginning to embrace robots, with AI helping to create new movements and choreography, and with robots sharing the stage with human dancers.

The relationship between society and technology is yin and yang, with every massive enhancement accompanied by the potential for danger. AI, for example, offers the promise to end boring, repetitive jobs, enabling us to engage in higher level and more fulfilling tasks. It helps with any number of efficiency efforts, such as fraud detection, and it can even paint masterpiece artworks and compose symphonies. Sam Altman, CEO of OpenAI, hopes AI will unlock human potential and let us focus on the most interesting, most creative, most generative things.
2020-11-23 00:00:00 Read the full story…
Weighted Interest Score: 2.4650, Raw Interest Score: 1.0822,
Positive Sentiment: 0.4200, Negative Sentiment 0.1615

A vision for data driven lending

“The past is a foreign country – they do things differently there” – so runs the infamous first line of L.P. Hartley’s novel, The Go-Between. And so the unprecedented economic circumstances associated with Covid-19 are set to alter the lending landscape: lenders are dealing with material impairments, degradation of loan portfolios and spikes in forbearance requests. With the potential for cyclical lockdowns and business restarts on the horizon, lenders are having to monitor credit risk with limited visibility. Historic data sets previously used to drive a credit decision are less predictive of future circumstances.
Advanced data and analytics capabilities – the access to and mining of transactional data; real-time monitoring and advanced decisioning are integral to the solution. For traditional, incumbent institutions, the crisis represents perhaps the single most compelling opportunity to digitalise their systems. And for challenger banks and alternative lenders, an opportunity to steal a march: placing the plethora of open data sources available at the heart of their propositions.
2020-11-30 08:00:00 Read the full story…
Weighted Interest Score: 2.3681, Raw Interest Score: 1.3411,
Positive Sentiment: 0.3245, Negative Sentiment 0.2055

7 Things I Learned during My First Big Project as an ML Engineer

Important advice about machine learning from development to production

Apparently Covid-19 is not the only one that keeps on increasing significantly (hopefully it will end up pretty soon!), NLP research has also grown exponentially over the last couple of years.

One of my biggest mistakes was using only 1 method for any type of data. Simply because it works best with certain data. In other words, I only did an experiment at the beginning of the project. After finding the best method, I keep on…
2020-11-30 15:03:20.541000+00:00 Read the full story…
Weighted Interest Score: 2.2950, Raw Interest Score: 1.2547,
Positive Sentiment: 0.3326, Negative Sentiment 0.2721

IT Departments Find Timing is Good to Modernize Legacy Systems; AI Can Help

The pandemic era of increased remote work and powerful available AI is motivating IT departments to examine legacy software systems for renewal. A legacy application, as defined by Gartner, is “an information system that may be based on outdated technologies, but is critical to day-to-day operations.”

This process of renewal can also be called modernization and often involves a move from on-premises hardware to the cloud.

2020-11-24 22:59:42+00:00 Read the full story…
Weighted Interest Score: 2.1773, Raw Interest Score: 1.1020,
Positive Sentiment: 0.1090, Negative Sentiment 0.1453

AWS Infrastructure Solutions BrandVoice: How The Edge Drives Innovation In 3 Major Industries

Edge computing systems are managing an increasing amount of data as businesses across industries realize how the technology can drive innovation.

The edge represents locations beyond centralized clouds where data is either processed or analyzed close to where it’s created. In many cases, this data is generated by a device, such as a camera or sensor, that is connected to the Internet of Things.

2020-11-25 00:00:00 Read the full story…
Weighted Interest Score: 2.1248, Raw Interest Score: 1.1754,
Positive Sentiment: 0.2486, Negative Sentiment 0.1808

Data Management Best Practices for Machine Learning – Webinar

Machine learning is on the rise at businesses hungry for greater automation and intelligence. A recent study fielded amongst the subscribers of DBTA found that 48% currently have machine learning initiatives underway with another 20% considering adoption. At the same time, most projects are still in the early phases. Machine learning is the new kid on the block. From data quality issues, to architecting and optimizing models and data pipelines, there are many success factors to keep in mind.
2021-01-21 00:00:00 Read the full story…
Weighted Interest Score: 2.1106, Raw Interest Score: 1.7224,
Positive Sentiment: 0.4053, Negative Sentiment 0.1013

Improving virtual assistants’ performance using semantic search and Sentence Transformers

How is our well designed virtual assistant able to capture (or detect) the user intention? Here is where natural language understanding plays its role by performing the typical cognitive task that is extremely (sometimes) easy for humans and an authentic nightmare for computers.

The NLP/NLU techniques allow us to tackle this task in different ways. In the scope of the intent detection paradigm, we need to define the top…
2020-11-29 13:19:28.316000+00:00 Read the full story…
Weighted Interest Score: 1.9063, Raw Interest Score: 1.1499,
Positive Sentiment: 0.3493, Negative Sentiment 0.2475

Database Management Today: New Strategies and Technologies

From machine learning and automation, to hybrid and multicloud environments, technology trends continue to reshape the practice of database management. As a result, database professionals face new challenges and opportunities. Today, the average database team is tasked with managing more databases, bigger databases and a greater variety of databases – from the ground to the cloud. At the same time, businesses are hung…
2021-04-22 00:00:00 Read the full story…
Weighted Interest Score: 1.8834, Raw Interest Score: 1.4376,
Positive Sentiment: 0.3594, Negative Sentiment 0.1797

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