Industry News

Industry News

Best Deep Fakes Yet by Try Parker and Matt Stone of South Park : Benzinga Global Fintech Awards Tomorrow – see you there! : Consumer Reports – Tesla Autopilot “Distant Second” to GM’s Driver Assistance : Triggerless backdoors – The hidden threat of deep learning : How to Build a Twitter Sentiment Analysis System

AI & Machine Learning News. 09, November 2020

AI & Machine Learning News. 09, 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?

Best Deep Fakes Yet by Try Parker and Matt Stone of South Park

CloudQuant Thoughts : Irreverent, as you would expect from the creators of South Park, but the most impressive 15 minutes of Deep Fakes I have seen yet. With Donald Trump, Al Gore, Ivanka Trump, Mark Zuckerberg, Michael Caine, a truly amazing Sound of Music aged Julie Andrews, Chris Wallace, Anthony Fauci, Jared Kushner,

CloudQuant will be at the Benzinga Global Fintech Awards TOMORROW – November 10th 2020

CloudQuant will be attending the Benzinga Global Fintech Awards, Tomorrow – November 10th 2020.
Our Industry leading Alternative Data Fabric “Liberator” is up for an industry award, our CEO Morgan Slade will be taking part in a fireside chat and our sales team will be available throughout the event to answer your questions and discuss our huge range of alternative datasets. Look forward to seeing you there.
Read the Full Story…

Consumer Reports: Tesla Autopilot “Distant Second” to GM’s Driver Assistance

“The evidence is clear: If a car makes it easier for people to take their attention off the road, they’re going to do so.”
Nonprofit product testing group Consumer Reports has determined that Tesla’s Autopilot driving assistance feature is a “distant second” to General Motors’ Super Cruise.
Consumer Reports tested a total of 17 vehicles equipped with a variety of active driving assistance systems — which, as Consumer Reports emphasized, is still not the same as a fully autonomous vehicle. “To be clear, active driving assistance doesn’t make a car ‘self-driving,’ but rather it’s intended to support the driver — a well-designed system can help relieve driver fatigue and stress, such as on long highway road trips or in stop-and-go traffic,” writes Consumer Reports. A Cadillac CT6 equipped with Super Cruise, a hands-free driving feature that’s designed to make highway commutes more convenient, beat a Tesla Model Y with Autopilot when it came to safety and keeping the driver engaged.
2020-10-28 Read the Full Story…
CloudQuant Thoughts : For “ACTIVE ASSISTANCE” which is not the same as fully autonomous. Tesla are definitely closer to Full Autonomy but I would not trust any system to take complete control just yet!

Triggerless backdoors: The hidden threat of deep learning

Hackers can implant backdoors on deep neural networks without leaving a trace, researchers at the Germany-based CISPA Helmholtz Center for Information Security have found
This article is part of our reviews of AI research papers, a series of posts that explore the latest findings in artificial intelligence.
In the past few years, researchers have shown growing interest in the security of artificial intelligence systems. There’s a special interest in how malicious actors can attack and compromise machine learning algorithms, the subset of AI that is being increasingly used in different domains.
Among the security issues being studied are backdoor attacks, in which a bad actor hides malicious behavior in a machine learning model during the training phase and activates it when the AI enters production.
2020-11-05 Read the Full Story…
CloudQuant Thoughts : Triggerless backdoor, misidentification of a turtle as a rifle, the many stories of tricking Teslas to change the speed limit or stop at a sign that was not a stop sign. We still have a long way to go before we can have secure but easily updated AI.

How to Build a Twitter Sentiment Analysis System

In the field of social media data analytics, one popular area of research is the sentiment analysis of twitter data. Twitter is one of the most popular social media platforms in the world, with 330 million monthly active users and 500 million tweets sent each day. By carefully analyzing the sentiment of these tweets—whether they are positive, negative, or neutral, for example—we can learn a lot about how people feel about certain topics.
Understanding the sentiment of tweets is important for a variety of reasons: business marketing, politics, public behavior analysis, and information gathering are just a few examples. Sentiment analysis of twitter data can help marketers understand the customer response to product launches and marketing campaigns, and it can also help political parties understand the public response to policy changes or announcements.
However, Twitter data analysis is no simple task. There are something like ~6000 tweets released every second. That’s a lot of Twitter data! And though it’s easy for humans to interpret the sentiment of a tweet, human sentiment analysis is simply not scalable. In this article, we’re going to look at building a scalable system for Twitter sentiment analysis, to help us better understand the role of machine learning in social media data analytics.
2020-11-05 Read the Full Story…
CloudQuant Thoughts : A neat beginners to mid level intro to Sentiment Analysis.

Guided Labeling Episode 6: Comparing Active Learning with Weak Supervision

Welcome to the sixth episode of our Guided Labeling Blog Series. In the last episode, we made an analogy with a number of “friends” labeling “movies” with three different outcomes: “good movie” (?), “not seen movie” ( – ), “bad movie” (?). We have seen how we can train a machine learning model, also predicting movies no friend has watched before and adding to the model additional feature data about such movies.
The other episodes are here:

  • 1: An Introduction to Active Learning
  • 2: Label Density
  • 3: Model Uncertainty
  • 4: From Exploration to Exploitation
  • 5: Blending Knowledge with Weak Supervision

Let’s pick up where we left off. You can blend friends’ movie opinions into a single model, but how is this useful if you don’t have any labels to train a generic supervised model? How can weak supervision become an alternative to active learning in a generic classification task? How can this analogy with many “friends” labeling “movies” work better than a single human expert like in active learning?

2020-11-06 08:30:58+00:00 Read the full story…
Weighted Interest Score: 2.6458, Raw Interest Score: 1.3082,
Positive Sentiment: 0.1595, Negative Sentiment 0.0638

Deutsche Börse Invests In Clarity AI

Clarity AI announced that it has closed a USD $15 million funding round led by Deutsche Börse AG and co-investor Mundi Ventures. Clarity AI empowers investors to manage the impact of their portfolios through a proprietary technology platform that leverages big data and machine learning to assess sustainability for all societal stakeholders.
“Our purpose is simple: to measure the impact of companies on our society and planet,” said Rebeca Minguela, Founder and CEO of Clarity AI. “Investors attempting to evaluate impact have faced fragmented and unreliable data, inconsistent subjective definitions, and a lack of standards and tools for comprehensive analysis. Historically it has been too hard and resource-intensive to get accurate and transparent insights. Clarity AI provides a solution for that.”

2020-11-05 06:14:35+00:00 Read the full story…
Weighted Interest Score: 4.8108, Raw Interest Score: 2.0093,
Positive Sentiment: 0.4206, Negative Sentiment 0.1636

Financial Institutions Benefit from AI, But Consumers Remain Skeptical

There’s no doubt that retail banking leaders understand the potential of artificial intelligence technology to improve customer experience. Nearly every one (94%) of more than 300 banking and insurance executives surveyed by The Capgemini Research Institute agreed that improving CX is the key objective behind launching new AI-enabled initiatives.

In fact, more than half of the international sample say that at least 40% of customer interactions are already enabled by various AI applications, including conversational agents, prescriptive modeling, process automation, and complex analytics.
2020-11-09 02:35:04+00:00 Read the full story…
Weighted Interest Score: 4.7914, Raw Interest Score: 1.8185,
Positive Sentiment: 0.3550, Negative Sentiment 0.2078

Intel Buys Another AI Startup

Intel Corp. has quietly acquired another AI platform developer, Israeli-based
The acquisition, confirmed by Intel late Tuesday (Nov. 3) to the web site, is the latest in a flurry of deals by the chip maker (NASDAQ: INTC) as it fills out its AI and machine learning portfolio.
Terms of the acquisition were not disclosed. Intel did say the would continue to operate as an independent company.
Founded in 2016, Jerusalem-based’s data science platform is designed to help development teams manage and scale AI models. Early backers include Prashant Malik, co-creator of the Cassandra NoSQL database management system.
The startup’s most recent release is an MLOps dashboard designed to boost machine learning server utilization, a gap the startup refers to as “computational debt.” The framework is designed to boost utilization of CPUs, graphics processors and memory resources by as much as 80 percent.

2020-11-03 Read the Full Story(TheTechee)…
2020-11-04 00:00:00 Read the full story… (Datanami)
2020-11-07 10:30:24+00:00 Read the full story…(AnalyticsIndia)
Weighted Interest Score: 4.5634, Raw Interest Score: 2.3526,
Positive Sentiment: 0.1222, Negative Sentiment 0.1222

Intel To Purchase SigOpt, An AI Software Optimisation Platform

Intel has announced that the company is planning to acquire a San Francisco-based software optimisation startup — SigOpt. The company further stated that the terms of the deal are expected to close this quarter, however, weren’t disclosed to the media.

According to the company’s statement to the media, Intel is planning to leverage SigOpt’s technologies across its products to accelerate, amplify, and scale AI software tools for developers. The company further stated that SigOpt’s software technologies would be combined with Intel hardware products to gain a competitive advantage and provide differentiated value for data scientists and developers.
2020-11-02 14:41:24+00:00 Read the full story…
Weighted Interest Score: 3.7229, Raw Interest Score: 1.9669,
Positive Sentiment: 0.1380, Negative Sentiment 0.1035

Microsoft Partners With Adobe and on Advanced CRM

Microsoft Corporation (MSFT) has announced a partnership with Adobe Inc. (ADBE) and privately held startup to offer customer relationship management (CRM) software solutions utilizing artificial intelligence (AI).
“This year has made clear that businesses fortified by digital technology are more resilient and more capable of transforming when faced with sweeping changes like those we are experiencing,” said Satya Nadella, CEO of Microsoft. “Together with and Adobe, we are bringing to market a new class of industry-specific AI solutions,” he added.1
The joint effort is designed to leverage the combined resources of the three partners to mount a more effective challenge to the dominance of in the CRM field than any of them could achieve individually. According to research firm Gartner Inc. (IT), salesforce is the top vendor of CRM software, with a 20% share of this $56 billion market in 2019, while Microsoft’s Dynamics 365 offering registered a mere 2.6% market share.

2020-11-05 19:42:48.140000+00:00 Read the full story…
Weighted Interest Score: 4.2816, Raw Interest Score: 1.7321,
Positive Sentiment: 0.1732, Negative Sentiment 0.0577

AI Can Make Bank Loans More Fair

As banks increasingly deploy artificial intelligence tools to make credit decisions, they are having to revisit an unwelcome fact about the practice of lending: Historically, it has been riddled with biases against protected characteristics, such as race, gender, and sexual orientation. Such biases are evident in institutions’ choices in terms of who gets credit and on what terms. In this context, relying on algorithms to make credit decisions instead of deferring to human judgment seems like an obvious fix. What machines lack in warmth, they surely make up for in objectivity, right?
Sadly, what’s true in theory has not been borne out in practice. Lenders often find that artificial-intelligence-based engines exhibit many of the same biases as humans. They’ve often been fed on a diet of biased credit decision data, drawn from decades of inequities in housing and lending markets. Left unchecked, they threaten to perpetuate prejudice in financial decisions and extend the world’s wealth gaps.

2020-11-06 13:25:26+00:00 Read the full story…
Weighted Interest Score: 3.6202, Raw Interest Score: 1.4401,
Positive Sentiment: 0.1290, Negative Sentiment 0.2472

Green Data Centers Seen as Helping Manage AI Power Demands

Huawei has built a cloud data center in Ulanqab; it is holding out to be a model green data center. The Chinese multinational technology company published the account in a sponsored post in The Register, describing its efforts to build the new data center in Ulanqab, a city in Mongolia.
Power usage effectiveness (PUE), is seen as a measure of “greenness” or energy efficiency, PUE was introduced in 2006 by the Green Grid, a non-profit organization of IT professionals; it has become the most commonly used metric for reporting the energy efficiency of data centers. The higher the value, the less the efficiency.
Huawei reports its cloud data center in Ulanqab achieves an annual PUE as low as 1.15, compared to an average PUE of 1.58 in 2020, according to the Uptime Institute. “Data Efficiency Gains Have Flattened Out,” stated the headline on a line chart showing gains in energy efficiency from 2007 to 2013, and an essentially flat line since then.

2020-11-05 22:35:01+00:00 Read the full story…
Weighted Interest Score: 3.5211, Raw Interest Score: 1.9531,
Positive Sentiment: 0.2155, Negative Sentiment 0.0404

How to create stunning visualizations using python from scratch

A step-by-step guide using Matplotlib and Seaborn libraries

Visualization is an important skill set for a data scientist. A good visualization can help in clearly communicating insights identified in the analysis also it is a good technique to better understand the dataset. Our brain is wired in a way that makes it easy for us to extract patterns or trends from visual data as compared to extracting details based on reading or other means.

In this article, I will be covering the visualization concept from the basics using python. Below are the steps to learn visualization from basic,

  1. Importing data
  2. Basic visualization using Matplotlib
  3. More advanced visualizations, still using Matplotlib
  4. Building quick visualizations for data analysis using Seaborn
  5. Building interactive charts

2020-11-08 03:30:36.025000+00:00 Read the full story…
Weighted Interest Score: 3.4597, Raw Interest Score: 1.3460,
Positive Sentiment: 0.1706, Negative Sentiment 0.0379

Software Suppliers Responding to Market Opportunity for AI in Government

The US government has woken up to the importance of AI, the work of AI scientists is paying off, and the investment community is supporting the industry. AI entrepreneurs and suppliers of AI-related products and services are staring at a market poised for dramatic growth.
Perspective is called for. “What we need to be more concerned about is the thoughtfulness around everything that we do,” suggested Joanne Lo, PhD, the CEO of Attica AI, speaking on data governance challenges at the opening morning of the 2nd Annual AI World Government conference and expo held virtually last week. Attica said her company designs systems tools for the DoD and first responders.
She spends time with clients assessing the foundation in place that new AI systems will be built to support. “We tell the client to think about the foundation, to clean up the data before we can build something new on top of it, so it does not crumble,” she said.
Her company also encourages clients to think beyond making applications work faster, on bigger computers and with faster chips. “We are working on those, and they should be done, but we say you need to think about what you as a human have to offer. What is the human 2.0 you can become,” she said.

2020-11-05 22:53:25+00:00 Read the full story…
Weighted Interest Score: 3.4308, Raw Interest Score: 1.6063,
Positive Sentiment: 0.1452, Negative Sentiment 0.1724

What Does a Data Engineer’s Career Path Look Like?

Big data is changing the future of almost every industry. The market for big data is expected to reach $23.5 billion by 2025.

Data science is an increasingly attractive career path for many people. However, the outlook is hazy for people that are not as familiar with the career path.

If you want to become a data scientist, then you should start by looking at the career options available. Northwestern University has a great list of ways that people can pursue a career in data science. You should also learn the career path that you need to follow to get started, which includes learning the right programming languages.
2020-11-08 19:38:27+00:00 Read the full story…
Weighted Interest Score: 3.3677, Raw Interest Score: 2.0106,
Positive Sentiment: 0.2011, Negative Sentiment 0.1005

Algorithmia, Datadog Team on MLOps

Tools continue to be introduced to allow machine learning developers to monitor model and application performance as well as anomalies like model and data drift—a trend one market tracker dubs “ModelOps.”
The latest comes from Algorithmia, which this week launched an enterprise platform for monitoring machine learning model performance. The Seattle-based MLOps and management software vendor also said it has partnered with DataDog on a pipeline designed to stream metrics through Apache Kafka to Datadog via its Metrics API.
Algorithmia’s enterprise platform provides access to algorithm inference and MLOps metrics. Along with improving model performance and compliance with data governance regulations, Algorithmia CEO Diego Oppenheimer said the monitoring platform helps reduce the risk of model failure.
2020-11-05 00:00:00 Read the full story…
Weighted Interest Score: 3.3480, Raw Interest Score: 1.8650,
Positive Sentiment: 0.1356, Negative Sentiment 0.3391

Must-Have Elements of a Modern Data Approach

he ability to embed analytics within every element of the modern data platform provides business leaders with the capabilities to understand information in context and achieve situational awareness to act in the moment. For example, high-performance predictive and machine learning algorithms can reveal meaningful patterns in data and build applications that automate manual business processes. With search capability analysis that helps extracts insights from unstructured data, business leaders can generate conclusions and more importantly, apply them to the business, faster than ever before.

2020-11-04 00:00:00 Read the full story…
Weighted Interest Score: 3.3110, Raw Interest Score: 1.8358,
Positive Sentiment: 0.3750, Negative Sentiment 0.0197

Digital Banking Strategies Hampered By AI Talent Gap

There is a massive increase in demand for data, advanced analytics and AI skills in the banking industry at the same time as there is a lack of data and AI talent within most organizations. This gap between need and availability of talent shows no signs of abating. The solution is to combine outsourced solutions with a focus on upskilling your workforce.
In research done by the Digital Banking Report, financial organizations of all sizes indicate a low level of data maturity despite an increasing array of AI solutions offered by third-party vendors. In the research, only 12% of organizations believed they were “very effective” or “extremely effective” at using data and advanced analytics. This is lower than prior to the onset of COVID-19. While legacy systems are cited as the primary reason for the shortfall, the second most cited challenge is the lack of expertise within the organization to deploy AI technology effectively.
In other words, while banks and credit unions can purchase sophisticated AI solutions, there usually isn’t a defined path to achieving strategic goals or increasing business value. This creates a digital transformation paradox; where decision makers and employees believe in the power of data and AI, yet the appropriate actions are not being taken to leverage these tools for the benefit of digital transformation solutions.
2020-11-02 00:05:37+00:00 Read the full story…
Weighted Interest Score: 3.2499, Raw Interest Score: 1.5065,
Positive Sentiment: 0.2912, Negative Sentiment 0.1772

Japan’s SoftBank back in the black as investments improve

Son said his investments will focus on AI, or artificial intelligence, which he said will prove vital to all the companies he’s banking on, like robots doing deliveries and automated driving.
“We used to say whoever rules the mobile net will rule the net,” he said. “We think whoever rules AI will rule the future.”

2020-11-09 00:00:00 Read the full story…
Weighted Interest Score: 3.0568, Raw Interest Score: 1.6733,
Positive Sentiment: 0.2390, Negative Sentiment 0.1992

Forrester: Top Emerging Technology Trends To Watch In 2021 And Beyond

According to Forrester (FORR: NASDAQ), the next decade will require CIOs to both respond to digital acceleration and proactively manage uncertainty. Rapidly changing consumer trends, complex security concerns, the ethical use of artificial intelligence, and the increasing impacts of climate change will drive businesses to incorporate systemic risk into their long-term planning.
The Forrester report “Top Trends And Emerging Technologies, Q3 2020” highlights important trends and organizes emerging technologies into seven key domains that will play a big role in accelerating this shift: artificial intelligence; business automation and robotics; enterprise risk management; human experience and productivity; new compute architectures; next-generation communications; and Zero Trust security. Key trends include:

  • Rising demand for ethical AI.
  • Recasting of automation roadmaps.
  • Moving toward hyperlocal business operations.
  • Driving innovation everywhere using cloud-native technologies.
  • Shifting cloud strategies toward the edge.

2021-11-09 00:00:00 Read the full story…
Weighted Interest Score: 3.0519, Raw Interest Score: 1.4881,
Positive Sentiment: 0.2790, Negative Sentiment 0.0775

Provizio closes $6.2M seed round for its car safety platform using sensors and AI – TechCrunch

Provizio, a combination hardware and software startup with technology to improve car safety, has closed a seed investment round of $6.2million. Investors include Bobby Hambrick (the founder of Autonomous Stuff); the founders of Movidius; the European Innovation Council (EIC); ACT Venture Capital.

The startup has a “five-dimensional” sensory platform that — it says — perceives, predicts and prevents car accidents in real time and beyond the line-of-sight. Its “Accident Prevention Technology Platform” combines proprietary vision sensors, machine learning and radar with ultra-long range and foresight capabilities to prevent collisions at high speed and in all weather conditions, says the company. The Provizio team is made up of experts in robotics, AI and vision and radar sensor development.
2020-11-06 00:00:00 Read the full story…
Weighted Interest Score: 3.0395, Raw Interest Score: 1.5221,
Positive Sentiment: 0.2283, Negative Sentiment 0.6088

Yann LeCun’s Deep Learning Course Is Now Free & Fully Online

Yann LeCun’s deep learning course — Deep Learning DS-GA 1008 — at NYU Centre for Data Science has been made free and accessible online for all. The course will be led by Yann LeCun himself, along with Alfredo Canziani, an assistant professor of computer science at NYU, in Spring 2020.
This deep learning course will focus on the latest techniques in deep learning and representation learning. It will also focus on an in-depth understanding of supervised and unsupervised deep learning, embedding methods, metric learning, and convolutional and recurrent nets. The course will further talk about the applications to computer vision, natural language understanding, and speech recognition. The course, however, comes with a prerequisite of completing the introductory course of deep learning — DS-GA 1001 Intro to Data Science or a graduate-level machine learning course.

2020-11-09 09:58:10+00:00 Read the full story…
Weighted Interest Score: 3.0379, Raw Interest Score: 2.1832,
Positive Sentiment: 0.0716, Negative Sentiment 0.0000

AMD Acquires Xilinx To Bolster Its HPC Portfolio

Last week, AMD entered into agreement to acquire chip manufacturer Xilinx through a $35 billion all-stock transaction. This is the latest gargantuan M&A deal in the semiconductor market to focus on new opportunities in processing, after the NVIDIA-Arm announcement. The AMD-Xilinx partnership is meant to broaden AMD’s product portfolio, which focuses on high-performance CPUs and GPUs (graphics processing units). AMD CEO Lisa Su aims to establish the company as “the industry’s high-performance computing leader.” That would mean expanding AMD into a customer audience that Xilinx currently leads in high-performance field programmable gate array (FPGA) and system on a chip (SoC) components for data centers. These products target verticals that include communication, automotive, and defense markets. FPGAs can also serve AI infrastructure for machine learning (ML) inferencing.
Although the two companies differ in product and markets, the product lines will be complementary. AMD anticipates US$300 million in synergistic revenue. Short term, the companies will focus on combining go-to-market strategies. However, it’s likely they’ll start combining IP for their respective SoCs — such as Xilinx’s adaptable SoCs — to use more of AMD’s processor and GPU IP. Both companies face an enormous opportunity in AI. Expect a leadership position in high-performance computing (HPC) to also mean a massive stake in AI infrastructure. HPC is comprised of clusters of computational nodes conjoined with high volumes of storage and bandwidth to handle very complex scientific, engineering, and artificial intelligence workloads.
2020-11-04 01:22:11-05:00 Read the full story…
Weighted Interest Score: 3.0204, Raw Interest Score: 1.4936,
Positive Sentiment: 0.1821, Negative Sentiment 0.0729

HR-Tech Startup Leena AI Raises $8M In Series A Funding To Accelerate Hiring & Product Development

Leena AI, an artificial intelligence-based employee experience platform, has announced an $8 million funding in Series A round led by Greycroft. According to the official release of the company, the funding is going to be used for expanding its go-to-market programs, hiring talent as well as accelerating product development.

In 2018 the company, Leena AI, was working on building an intelligent virtual assistant, aka chatbot, for addressing Human Resources-related issues. However, recently, the company has moved its focus on to more broader HR service delivery. This funding would allow Leena AI to continue its growth and momentum and would enable them to continue developing solutions to modernise legacy employee experience.
2020-11-04 07:59:54+00:00 Read the full story…
Weighted Interest Score: 3.0042, Raw Interest Score: 1.3091,
Positive Sentiment: 0.2083, Negative Sentiment 0.1190

The cutting-edge computer architecture that’s changing the AI game (VB Live)

AI and machine learning demand new approaches to computer architecture — but, of course, there are more factors. Large amounts of data, the arrival of industry-standard frameworks such as TensorFlow and PyTorch, and the death of Moore’s Law, are all signs that it’s time for the next generation of computing systems. And it’s one of the biggest transitions that the computer industry has seen since the changes demanded by the Internet and online connectivity.
The new wave of computer architecture is being driven by three main issues:

  • First, data centers are growing larger, the amount of data that needs to be processed is growing exponentially, and compute is getting more expensive, which means companies need new more effective, powerful, and efficient architectures for data processing.
  • Second is the difficulty — in time, expense, and resources — of turning that massive amount of data into actual value for a business. The companies that manage this transmutation will have a dramatic competitive edge over the ones that are falling behind.
  • Third, applications are evolving in sophistication and ability, and companies want the computing architecture that allows them to take advantage of these new possibilities.

2020-11-05 00:00:00 Read the full story…
Weighted Interest Score: 2.8640, Raw Interest Score: 1.5131,
Positive Sentiment: 0.2124, Negative Sentiment 0.0796

Brainome Right-Sizes Your Data Before ML Training

A startup called Brainome today launched a new product designed to help data scientists determine how much data they need to sufficiently train their machine learning models. In addition to cutting costs, the software can also help data scientists avoid overfitting their models.
Called Daimensions, the Python-based tool essentially works as a compiler that generates the “memory equivalent capacity” of one’s data. Based on this figure, data scientists can whether there’s enough data to extract a meaningful signal. The tool also tells the user about the capability to generalize from the data, and also helps them with feature selection.

2020-11-04 00:00:00 Read the full story…
Weighted Interest Score: 2.8502, Raw Interest Score: 1.7268,
Positive Sentiment: 0.0714, Negative Sentiment 0.1998

Data Lakes Are Legacy Tech, Fivetran CEO Says

By some accounts, data lakes appear poised to supplant data warehouses as the center of gravity of modern analytics systems, particularly with today’s sophisticated data virtualization capabilities. But with the advent of cloud data warehouses that separate compute and storage, companies should take a hard look at their data lakes.
That’s the message that Fivetran CEO and co-founder George Fraser delivered during his keynote address for the “Modern Data Stack Conference 2020,” his company’s virtual conference that took place two weeks ago.
“In my opinion, data lakes are not part of the modern data stack. Data lakes are legacy,” Fraser said. “There are organizational [and] quasi-political reasons why people adopt data lakes. But there are no longer technical reason for adopting data lakes.”
2020-11-05 00:00:00 Read the full story…
Weighted Interest Score: 2.7517, Raw Interest Score: 1.6673,
Positive Sentiment: 0.3176, Negative Sentiment 0.1059

How to Approach Technology From a Non-Computing Background

If you’re a professional in another field who’s interested in a career as a technologist, we have good news for you: It’s very possible to plunge into learning the technology specialization of your choice without any previous tech experience. For example, you might have a background as a marketer or political scientist, and realize you need to build up your programming or data-science skills to further your career—don’t be intimidated about jumping in.
The term for this is a “non-tech” or “non-computing” background. That means a working knowledge of tech but little working experience when it comes to programming, data algorithms and data structures, according to Tiffani L. Williams, teaching professor and director of onramp programs at the University of Illinois at Urbana-Champaign.
2020-11-04 00:00:00 Read the full story…
Weighted Interest Score: 2.7021, Raw Interest Score: 1.4221,
Positive Sentiment: 0.1659, Negative Sentiment 0.0237

Insurance giant leads $13.6m funding round into Oxford University AI spin-out

An Oxford University spin-out which specialises in one of the most promising applications of artificial intelligence (AI) has raised $13.6m (£10.4m) in funding.

Mind Foundry develops AI for sectors including financial services and aerospace, specialising in the development of machine learning, in which computer systems gradually teach themselves to carry out tasks like identifying data.

The company raised the funding from Aioi Nissay Dowa Insurance, part of Japanese insurance giant MS&AD. Other backers includ…
2020-11-09 00:00:00 Read the full story…
Weighted Interest Score: 2.6838, Raw Interest Score: 1.2288,
Positive Sentiment: 0.1755, Negative Sentiment 0.1170

An Important Guide To Unsupervised Machine Learning

It’s become very clear that unsupervised machine learning and artificial intelligence can be very helpful for business growth, but how do they work? There are some key methods you’ll want to know so your market research, trend predictions, and other machine learning uses are effective.
And that digital transformation is being introduced by high-tech solutions. Hence, it comes as no surprise that mundane business tasks are being completely taken over by tech advancements. Machines, artificial intelligence (AI), and unsupervised learning are reshaping the way businesses vie for a place under the sun. With that being said, let’s have a closer look at how unsupervised machine learning is omnipresent in all industries.
2020-11-01 18:08:28+00:00 Read the full story…
Weighted Interest Score: 5.1657, Raw Interest Score: 1.9476,
Positive Sentiment: 0.1885, Negative Sentiment 0.0628

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