AI & Machine Learning News. 31, August 2020
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?
This AI Removes Shadows From Your Photos!
A team of computer scientists from Google, MIT, and the University of California, Berkeley have created an impressive AI-powered “shadow removal” tool that can realistically remove harsh shadows from portraits, while leaving natural shadows intact. The results are impressive.
The paper describing this technology was originally published back in May, but it started to draw more attention this past weekend when the YouTube channel Two Minute Papers picked it up.
CloudQuant Thoughts : Would it be pedantic to point out that this particular 2 minute papers video is actually 6 minutes long and longer than the original video put out by Xuaner (Cecilia) Zhang? Link to the Project Website.
Eric Schmidt: China could be AI’s superpower if we don’t act now
Ex-Google CEO Eric Schmidt is sounding the alarm about the implications of China pulling ahead of the U.S. in artificial intelligence research and development. Speaking on a Bipartisan Policy Center webcast on Tuesday, he said the U.S. lacks a long-term plan to win the AI race, and lacks government funding for the basic research the U.S. will need to stay ahead of the Chinese.
“China is on its way to surpass us in many, many ways, and they’re cleverly run in a way that’s different from the way we would ever want to run,” says Schmidt, who is currently chair of the U.S. Department of Defense’s Defense Innovation Advisory Board. “We need to take them seriously. . . they’re going to end up with a bigger economy, more R&D investments, better quality research, wider applications of technology, and a stronger computing infrastructure.”
“THE CHINESE MODEL IS A VISION OF HIGH-TECH AUTHORITARIANISM.”
The Chinese government hasn’t been secretive about its ambitions. The country’s Belt and Road initiative, announced in 2013, is a sprawling plan to make China a rival to the U.S. as an economic superpower. A major part of that effort involves large investments in Chinese AI talent and research. China fully believes it will soon overtake the U.S. in AI, and that it may be able to leverage that lead to become the world’s dominant trade and commerce center.
2020-08-27 00:00:29 Read the full story…
Weighted Interest Score: 3.1862, Raw Interest Score: 1.4209,
Positive Sentiment: 0.2304, Negative Sentiment 0.1920
CloudQuant Thoughts : This should be the number one discussion in the current election.. It will not be on the list of topics to be discussed.
Amazon Launches an AI-Powered Health and Wellness Band Called Halo
The Halo Band only needs to be recharged once a week and costs $65, including six months of membership.
Amazon has today signaled its intention to grab a share of the fitness tracker market by introducing Amazon Halo — a health and wellness band combined with a “suite of AI-powered health features” accessed via the Halo app on Android or iOS.
Amazon is taking a different approach to fitness trackers by opting not to include a display on the Halo Band. Instead, a small “sensor capsule” is used to house an accelerometer, temperature sensor, heart rate monitor, two microphones, an LED indicator light and a button for turning the microphones on or off, but it can be used to perform other actions, too. No display also means great battery life, with Amazon claiming up to seven days between charges and a full recharge only takes 90 minutes.
2020-08-27 Read the full story…
CloudQuant Thoughts : With my recent lack of exercise due tot he pandemic, I am very keen to have a smart device that can track my health day and night, but do I really want one from Amazon? And particularly one that has a “membership” and two microphones that can “analyze your voice using machine learning for energy and positivity”. I am already feeling sick.
What a Biden-Harris administration might mean for AI’s future
If the Biden-Harris ticket wins in November, it will mark the first time that a sitting vice president is a digital native. Not only did Kamala Harris grow up digital, but she’s also spent much of her adult life in and around Silicon Valley, and her statewide campaigns have been backed by some of Silicon Valley’s top Democratic power brokers, including Sheryl Sandberg, Facebook’s chief operating officer, and Marc Benioff, chief executive of Salesforce.
But being a digital native doesn’t necessarily mean that Harris will chart a wise course when it comes to regulating technology, particularly AI and facial recognition. As someone who has founded two AI startups, holds a dozen AI-related patents, and has worked on more than 1,000 AI projects, I can tell you that the way AI behaves in the laboratory is very different from what happens when you unleash AI into the real world. While Harris has proven that she understands the importance of this technology, it’s not clear how a Biden-Harris administration would regulate AI.
2020-08-28 07:00:51 Read the full story…
Weighted Interest Score: 3.1010, Raw Interest Score: 1.3291,
Positive Sentiment: 0.1063, Negative Sentiment 0.2658
CloudQuant Thoughts : No politician will understand the importance of AI, they can barely understand the Internet. But this must be a topic that is raised in the public square as often as possible. What we do with AI, what we regulate and how that relates to what China is using it for.
Google researchers investigate how transfer learning works
Transfer learning’s ability to store knowledge gained while solving a problem and apply it to a related problem has attracted considerable attention. But despite recent breakthroughs, no one fully understands what enables a successful transfer and which parts of algorithms are responsible for it.
That’s why Google researchers sought to develop analysis techniques tailored to explainability challenges in transfer learning. In a new paper, they say their contributions help clear up a few of the mysteries around why machine learning models transfer successfully — or fail to.
During the first of several experiments in the study, the researchers sourced images from a medical imaging data set of chest X-rays (CheXpert) and sketches, clip art, and paintings from the open source DomainNet corpus. The team partitioned each image into equal-sized blocks and shuffled the blocks randomly, disrupting the images’ visual features, after which they compared agreements and disagreements between models trained from pretraining versus from scratch.
2020-08-27 00:00:00 Read the full story (VentureBeat)…
2020-08-31 08:30:37+00:00 Read the full story (Analytics India)…
Weighted Interest Score: 3.0017, Raw Interest Score: 1.5019,
Positive Sentiment: 0.4890, Negative Sentiment 0.3493
Detecting Pneumonia from Chest X-Rays with Deep Learning
Building various models, and using pre-trained models to diagnose pneumonia from a chest x-ray
n 2017, 2.56 million people died from pneumonia. About a third of those people were children less than 5 years old. The WHO estimated that 45,000 of these premature deaths were due to household air pollution. With more efficiency in the diagnostics, many of these deaths can be reduced.
The goal of this project is to create various machine learning and deep learning models so that when optimized, can assist radiologists in detecting Pneumonia from Chest X-Rays.
Environment and Tools : Throughout this project, we will be using python, so it is recommended that you have editors such as Google Colaboratory that is compatible with it but also allows the use of certain python packages.
We will be using python packages:
- Tensorflow (Version 1.x)
- Sci-Kit Learn and Keras
- Seaborn and Matplotlib
We will be using other packages to download files and helper functions that do not have to do with building the models. For a full list, check the code attached below.
2020-08-31 03:07:35.957000+00:00 Read the full story…
Weighted Interest Score: 2.8654, Raw Interest Score: 1.4156,
Positive Sentiment: 0.0363, Negative Sentiment 0.0363
The Problem with Big Data: It’s Getting Bigger
Take a quick look at the history of big data, and one fact will immediately strike you: The ability to collect data has almost always been larger than our ability to process it. Processing power used to expand exponentially, but in recent years that growth has slowed. The same cannot be said of the volumes of data available, which continue to grow year after year.
The figures on this are startling. More data was generated between 2014 and 2015 than in the entire previous history of the human race, and that amount of data is projected to double every two years. By 2020, it was projected that our accumulated digital data would grow to around 44 zettabytes (or 44 trillion gigabytes) and to 180 trillion gigabytes by 2025. Despite this concentrated effort to acquire data, less than 3 percent of it has ever been analyzed.
Whatever the other big data trends of 2020, then, one is arguably more important than all the rest: the sheer amount of data available and the problems that will cause us. In this article, we’ll look at just a few.
2020-08-28 07:30:07+00:00 Read the full story…
Weighted Interest Score: 2.6365, Raw Interest Score: 1.4032,
Positive Sentiment: 0.1183, Negative Sentiment 0.2198
Transformers: more than meets the AI – FinTech Futures – Podcast
The latest episode of the What the Fintech? podcast is brought to you remotely, featuring Matt Sattler, head of HSBC’s innovation labs.
On this episode, we examine the levels of M&A activity in the fintech space this year and dissect the financial losses of Revolut, Starling Bank and Monzo. Sattler reveals what it takes to land a commercial contract with the bank and offers his insight on how “data explainability” can help tackle bias in artificial intelligence.
Tune in to find out his eyebrow raising banished buzzword in another exciting rendition of ‘Fintech Jail’!
2020-08-25 11:15:59+00:00 Read the full story…
Weighted Interest Score: 9.6677, Raw Interest Score: 2.2155,
Positive Sentiment: 0.3021, Negative Sentiment 0.1007
3 Top Artificial Intelligence Stocks to Buy in September
Many people have probably heard of artificial intelligence but may be unsure exactly what AI entails.
AI occurs in two phases; the learning or training phase, in which case an algorithm is “taught” how to react to incoming information from troves of past data. The second phase is the “inference” phase, in which case a machine reacts to a prompt based on its learning without human interaction. Along the way, there’s quite a lot of software, processors, and memory that make all of this work, and there are a lot of companies directly or tangentially involved.
One thing’s for sure: The AI revolution is taking off and is bound to make many companies rich in the 2020s. Today, three of the best-positioned AI stocks are CrowdStrike (NASDAQ:CRWD), Alphabet (NASDAQ:GOOG) (NASDAQ:GOOGL), and Lam Research (NASDAQ:LRCX). Here’s why each is a solid buy in September.
2020-08-31 00:00:00 Read the full story…
Weighted Interest Score: 5.1341, Raw Interest Score: 1.8657,
Positive Sentiment: 0.3364, Negative Sentiment 0.1988
How Does PCA Dimension Reduction Work For Images?
In machine learning, we need lots of data to build an efficient model, but dealing with a larger dataset is not an easy task we need to work hard in preprocessing the data and as a data scientist we will come across a situation dealing with a large number of variables here PCA (principal component analysis) is dimension reduction technique helps in dealing with those problems.
In this article, we will demonstrate how to work on larger data and images using a famous dimension reduction technique PCA( principal component analysis).
Topics Covered in this article :
- How does PCA work?
- How does PCA work on Image compression?
- How does PCA work on a normal Dataset?
- Limitations of PCA
2020-08-30 07:30:00+00:00 Read the full story…
Weighted Interest Score: 4.7740, Raw Interest Score: 1.9247,
Positive Sentiment: 0.0846, Negative Sentiment 0.1269
Knowing The Difference Between Strong AI and Weak AI Is Useful And Applies To AI Autonomous Cars
Strong versus weak AI. Or, if you prefer, you can state it as weak versus strong AI (it’s Okay to be listed in either order, yet still has the same spice, as it were). If you’ve read much about AI in the popular press, the odds are that you’ve seen references to so-called strong AI and so-called weak AI, and yet both of those phrases are used wrongly and offer misleading and confounding impressions.
Time to set the record straight.
First, let’s consider what is being incorrectly stated. Some speak of weak AI as though it is AI that is wimpy and not up to the same capabilities as strong AI, including that weak AI is decidedly slower, or much less optimized, or otherwise inevitably and unarguably feebler in its AI capacities.
No, that’s not it.
Another form of distortion is to use “narrow” AI, which generally refers to AI that will only work in a narrowly-defined domain such as in a specific medical use or in a particular financial analysis use, and equate it with weak AI, while presumably strong AI is broader and more all-encompassing.
No, that’s not it either.
2020-08-27 21:21:50+00:00 Read the full story…
Weighted Interest Score: 4.2356, Raw Interest Score: 1.2986,
Positive Sentiment: 0.2555, Negative Sentiment 0.2182
Data Scientist: One Tech Role Immune From COVID-19?
If there’s a candidate with a skillset that can be sold to multiple employers in multiple sectors in late 2020, irrespective of COVID-19, it is the person with an elite education in data science and proven experience of extracting lucrative insights from real-world datasets. As a recruiter or a hiring manager, if you can find even one such data master, it is tantamount to hitting the jackpot. As a candidate who fits this description, you have the pick of employers.
This is especially true in banking and finance IT, where data scientists are present throughout organizations, from trading and research to HR. Data scientists are involved in everything from management decisions to cutting-edge machine learning projects.
But not all data scientists are made the same. The latest data science salary survey from recruitment firm Harnham puts entry-level data science salaries as low as £46,000 in the U.K. and $110,000 (for a data engineer) in the U.S.. By comparison, hedge funds can pay salaries as high as $200,000, but only for alpha generating data scientists at the top of their field. Dice’s own analysis of data-scientist salaries has shown a range of anywhere from $91,000 to roughly $170,000, depending on roles, experience and education; but such compensation can increase radically with specialization.
2020-08-26 00:00:00 Read the full story…
Weighted Interest Score: 4.1737, Raw Interest Score: 2.0880,
Positive Sentiment: 0.1975, Negative Sentiment 0.0564
Data Scientists Engaged in the Battle Against Data Bias
Data scientists have joined the battle to eliminate or at least identify the bias in datasets used to train AI programs.
The work is not easy. One person making the effort to address it is Benjamin Cox of H2O.ai, a firm dedicated to what it calls “responsible AI,” a blend of ethical AI, explainable AI, secure AI, and human-centered machine learning. With a background in data science and experience at Ernst & Young, Nike, and Citigroup, Cox is now a product marketing manager at H2O.
“I became deeply passionate about the field of responsible AI after years working in data science and realizing there was a considerable amount of work that needed to be done to prevent machine learning from perpetuating and driving systemic and economic inequality in the future,” Cox said in a r…
2020-08-27 21:41:03+00:00 Read the full story…
Weighted Interest Score: 3.9950, Raw Interest Score: 1.6373,
Positive Sentiment: 0.1979, Negative Sentiment 0.2879
Experts Advise Businesses to View AI More Strategically, Less Tactically
Organizations need to transition from opportunistic and tactical decision-making around AI to a more strategic focus, suggest leading business managers.
Two authors of a recent article in the MIT Sloan Management Review suggest the path to strategic AI for business can rest on three pillars. Amit Joshi is a professor of AI, analytics, and marketing strategy at IMD Business School in Switzerland, who works with companies in telecom, financial services, pharma, and manufacturing. Michael Wade is a professor of innovation and strategy at IMD Business School in Switzerland. His most recent book is “Orchestrating Transformation” from DBT Center Press, 2019.
The three pillars of strategic AI for business the authors suggest are:
- A robust and reliable technology infrastructure;
- New business models intended to bring the largest AI benefits; and
- Ethical AI
2020-08-27 21:36:09+00:00 Read the full story…
Weighted Interest Score: 3.8790, Raw Interest Score: 1.6502,
Positive Sentiment: 0.1800, Negative Sentiment 0.1950
Speech Recognition Gets an AutoML Training Tool
AutoML, the application of machine learning to create new automation tools, is branching out to new use cases, making itself useful for particularly tedious data science tasks when training speech recognition models.
Among the latest attempts at automating the data science workflow is an AutoML tool from Deepgram, offering what the speech recognition vendor claims is a new model training framework for machine transcription. The startup’s investors include Nvidia GPU Ventures and In-Q-Tel, the venture arm of the U.S. intelligence community.
Deepgram’s flagship platform scans audio data to train a speech recognition tool. Its deep learning tool uses a hybrid convolutional/recurrent neural network approach, training models via GPU accelerators.
2020-08-27 00:00:00 Read the full story…
Weighted Interest Score: 3.6313, Raw Interest Score: 2.2524,
Positive Sentiment: 0.1543, Negative Sentiment 0.0617
How to Turn a Data Policy into a Data Strategy
At Data Summit Connect 2020, DataStax VP Bryan Kirschner explained the consequences for organizations that generate and store data without developing a plan to leverage it.
“Many of you have probably been on a digital transformation journey, right? This CIO’s company is doing digital business at scale, but they don’t have a data strategy. He was very blunt: ‘I generate a terabyte of data a day, but I don’t have anything to do with it. So every month I just throw it away. I delete it. There’s no one studying this data, trying to turn it into significant value.’ They don’t have a data strategy. They have a data policy. They control the cost and risk of storing data, right? So they’re generating data, but they haven’t invested. And how we turn this data into value. So square one or square zero. What does it take to start to turn that data into significant value? So we talk to CEOs, our customers experts, and we came up with a list based on what we heard,” Kirschner said.
2020-08-26 00:00:00 Read the full story…
Weighted Interest Score: 3.5523, Raw Interest Score: 1.7438,
Positive Sentiment: 0.3018, Negative Sentiment 0.0335
Exploring Pathfinding Graph Algorithms
A deep dive into pathfinding algorithms available in Neo4j Graph Data Science library.
In the first part of the series, we constructed a knowledge graph of monuments located in Spain from WikiData API. Now we’ll put on our graph data science goggles and explore various pathfinding algorithms available in the Neo4j Graph Data Science library. To top it off, we’ll look at a brute force solution for a Santa Claus problem. Now, you might wonder what a Santa Claus problem is. It is a variation of the traveling salesman problem, except we don’t require the solution to end in the same city as it started. This is because of the Santa Claus’ ability to bend the time-space continuum and instantly fly back to the North Pole once he’s finished with delivering goodies.
- Infer spatial network of monuments
- Load the in-memory projected graph with cypher projection
- Weakly connected component algorithm
- Shortest path algorithm
- Yen’s k-shortest path algorithm
- Single source shortest paths algorithm
- Minimum spanning tree algorithm
- Random walk algorithm
- Traveling salesman problem
2020-08-30 19:01:18.286000+00:00 Read the full story…
Weighted Interest Score: 3.5089, Raw Interest Score: 1.0161,
Positive Sentiment: 0.0350, Negative Sentiment 0.1001
Digital Risk is Primary Focus for Corporate Boards in 2020 & Beyond
Digital risk continues to grow in importance for corporate boards as they recognize the critical nature of digital business transformation today. In fact, in Gartner’s 2020 Board of Directors survey, 67 per cent of respondents stated they view digital as the top business challenge for 2020 and 2021. Not only that, but 49 per cent of directors cite the need to reduce legal, compliance and reputation risk related to digital investments. For corpora…
2020-08-31 03:30:35+00:00 Read the full story…
Weighted Interest Score: 3.1726, Raw Interest Score: 2.6815,
Positive Sentiment: 0.1650, Negative Sentiment 0.1650
The Pivotal Role of Data for Managing Through Disruptive Times
Enterprises with forward-looking and well-honed data strategies will be able to navigate, and recover more quickly from, today’s turbulent economy than their less data-savvy counterparts. However, even leading tech-forward companies are struggling with ways to employ data resources to better reach their customers and markets.
These are among the conclusions of a survey of 500 executives, conducted in June by Longitude, a Financial Times company,…
2020-08-24 00:00:00 Read the full story…
Weighted Interest Score: 3.1537, Raw Interest Score: 1.7608,
Positive Sentiment: 0.4731, Negative Sentiment 0.5519
Unify Data Governance with Data Architecture
Think of an organization trying to create a single understanding of the information of the organization and the instances of that data around its estate. Consider different groups of people contributing to and using this model from different perspectives and varying reasons. And view this in the context of Data Governance, Data Architecture or Business Intelligence. A seemingly simple task becomes as complicated as six blind men building a model …
2020-08-25 07:35:17+00:00 Read the full story…
Weighted Interest Score: 3.1000, Raw Interest Score: 1.7851,
Positive Sentiment: 0.1380, Negative Sentiment 0.1012
5 Massively Important AI Features In Time Tracking Applications
Here’s how you can use AI features in your time tracking applications to take better note of how you’re using your most precious resource.
Artificial intelligence has transformed the future of many industries. One area that has been under- investigated is the use of AI in time tracking technology.
AI is Fundamentally Changing the Future of Time Tracking Technology
A time tracking software is a worthy investment irrespective of the size of your organization. It generates accurate reports based on the amount of time your team spends working on a task. These reports facilitate planning of budgets for upcoming projects.
2020-08-29 00:58:10+00:00 Read the full story…
Weighted Interest Score: 2.8033, Raw Interest Score: 1.5839,
Positive Sentiment: 0.3853, Negative Sentiment 0.1498
How to Fight Discrimination in AI
Is your artificial intelligence fair?
Thanks to the increasing adoption of AI, this has become a question that data scientists and legal personnel now routinely confront. Despite the significant resources companies have spent on responsible AI efforts in recent years, organizations still struggle with the day-to-day task of understanding how to operationalize fairness in AI.
So what should companies do to steer clear of employing discriminatory algorithms? They can start by looking to a host of legal and statistical precedents for measuring and ensuring algorithmic fairness. In particular, existing legal standards that derive from U.S. laws such as the Equal Credit Opportunity Act, the Civil Rights Act, and the Fair Housing Act and guidance from the Equal Employment Opportunity Commission can help to mitigate many of the discriminatory challenges posed by AI.
2020-08-28 12:35:59+00:00 Read the full story…
Weighted Interest Score: 2.7758, Raw Interest Score: 0.9780,
Positive Sentiment: 0.2977, Negative Sentiment 0.4678
How to Ensure Your AI Doesn’t Discriminate
Ensuring that your AI algorithm doesn’t unintentionally discriminate against particular groups is a complex undertaking. What makes it so difficult in practice is that it is often extremely challenging to truly remove all proxies for protected classes. Determining what constitutes unintentional discrimination at a statistical level is also far from straightforward. So what should companies do to steer clear of employing discriminatory algorithms?…
2020-08-28 12:35:59+00:00 Read the full story…
Weighted Interest Score: 2.7758, Raw Interest Score: 0.9780,
Positive Sentiment: 0.2977, Negative Sentiment 0.4678
Real Estate and Big Data: A Match Made in Heaven?
It’s easy to draw correlations between big data and certain areas of the business world. For example, it makes sense that a tech company would leverage big data, or that a software company would use it to develop a cutting edge SaaS offering. But in real estate, does big data really have a place at the table? Those inside the industry would say yes – and resoundingly so!
Big Data’s Role in Real Estate : Over the past 10 years, big data has played an increasingly important role in the real estate industry. Some of these influences can be clearly seen, while others fly beneath the radar. Let’s take a closer look at some of these interactions and what they mean for those inside the industry:
2020-08-30 11:04:41+00:00 Read the full story…
Weighted Interest Score: 2.7722, Raw Interest Score: 1.4974,
Positive Sentiment: 0.3238, Negative Sentiment 0.0405
DataRobot Report Claims Massive ROI for Its Enterprise AI Tools
Enterprise AI firm DataRobot has, in partnership with Forrester Consulting, released the results of a new study of the return on investment for DataRobot’s tools – and, perhaps unsurprisingly, DataRobot’s report has found that DataRobot’s products deliver an enormous return on investment (ROI). The “total economic impact study” assessed DataRobot’s financial impacts over the course of three years on a variety of organizations, finding an ROI of 514%.
Forrester’s total economic impact (or TEI) study aimed to “identify the cost, benefit, flexibility, and risk factors that affect the investment decision.” The process began with interviews with DataRobot stakeholders and interviews with four customer organizations, after which Forrester built a “composite organization” based on the characteristics of the interviewed customers. Then, Forrester constructed a model financial framework that attempted to capture the risks and concerns of the interviewed customers, measuring the costs and benefits of the DataRobot tools within this mock case study.
2020-08-26 00:00:00 Read the full story…
Weighted Interest Score: 2.7375, Raw Interest Score: 1.3961,
Positive Sentiment: 0.2737, Negative Sentiment 0.2190
Qlik Acquires Knarr Analytics to Increase Real-Time Collaboration
Qlik is acquiring the assets and IP of Knarr Analytics, an innovative start-up that provides real-time collaboration, data exploration and insight capture capabilities, to complement Qlik’s cloud data and analytics platform.
Acquiring Knarr Analytics advances Qlik’s vision of Active Intelligence, where technology and processes trigger immediate action from real-time, up-to-date data to accelerate business value across the entire data and analyti…
2020-08-27 00:00:00 Read the full story…
Weighted Interest Score: 2.6363, Raw Interest Score: 1.6995,
Positive Sentiment: 0.4025, Negative Sentiment 0.1342
The Top Trends in Data Management for 2021 – Webinar – Register
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: 2.5974, Raw Interest Score: 1.6729,
Positive Sentiment: 0.0929, Negative Sentiment 0.0929
LinkedIn Unveils Open-Source Toolkit for Detecting AI Bias
As AI becomes increasingly integrated in our day-to-day lives, the implications of bias in AI grow more and more worrisome. Training data that appears impartial is often influenced by historical and socioeconomic factors that render it biased, sometimes to the detriment of marginalized groups, and especially in AI applications in sectors like healthcare and criminal justice. Now, LinkedIn is introducing a tool to help combat these biases: the LinkedIn Fairness Toolkit, or LiFT.
LiFT is an open-source Scala/Spark library that LinkedIn says “enables the measurement of fairness, according to a multitude of fairness definitions, in large-scale machine learning workflows.” LinkedIn says that LiFT is both flexible and scalable, enabling use in scenarios ranging from exploratory analysis to production workflows and allowing the distribution of workloads over several nodes when handling large datasets.
2020-08-28 00:00:00 Read the full story…
Weighted Interest Score: 2.5797, Raw Interest Score: 1.0764,
Positive Sentiment: 0.2153, Negative Sentiment 0.2870
Modern Data Warehousing: Enterprise Must-Haves – Webinar – Registration
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 emerge…
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
ProteanTecs raises $45 million to apply AI to chip design and performance monitoring
ProteanTecs, which provides an AI platform to monitor chip reliability, today closed a $45 million funding round. The company says the fresh capital will bolster its go-to-market strategy and operations as it seeks to scale worldwide.
Chip design and manufacturing is a high-risk, high-reward pursuit. Mistakes made during the earliest phases are often enormously costly — chip fabrication plants cost billions to build. And the most sophisticated h…
2020-08-27 00:00:00 Read the full story…
Weighted Interest Score: 2.5183, Raw Interest Score: 1.5575,
Positive Sentiment: 0.0916, Negative Sentiment 0.3436
Why Is CRISP-DM Gaining Grounds
CRISP-DM is a popular methodology that follows a standard, end-to-end structured approach to solving a problem that requires data science. More precisely, CRISP-DM or CRoss-Industry Standard Process for Data Mining focuses on the data mining part of the operation.
Industries and organisations have been undergoing machine learning-driven approaches for a few years now. However, this report from last year suggests that 85% of AI projects won’t deliver for their sponsors due to reasons like low quality, lack of development process, l…
2020-08-31 04:30:26+00:00 Read the full story…
Weighted Interest Score: 2.4771, Raw Interest Score: 1.5240,
Positive Sentiment: 0.2505, Negative Sentiment 0.1461
Python Vs Scala For Apache Spark
Apache Spark is a popular open-source data processing framework. This widely-known big data platform provides several exciting features, such as graph processing, real-time processing, in-memory processing, batch processing and more quickly and easily.
With the expansion of data generation, organisations have started utilising these vast amounts of data to gain meaningful insights. Big data tools like Apache Spark helps in making sense of the data effectively.
2020-08-31 07:30:28+00:00 Read the full story…
Weighted Interest Score: 2.4545, Raw Interest Score: 1.8182,
Positive Sentiment: 0.2909, Negative Sentiment 0.0545
We got an exclusive look at the pitch deck that data startup GRID used to win investment from Uber backer NEA in a $12 million funding round
Data analytics startup GRID just raised $12 million in a Series A funding round backed by NEA, an early investor in Uber and Snap.
The data analytics market is growing rapidly, with forecasts suggesting it will be valued at $40 billion by 2023.
Forest Baskett, general partner at NEA, said GRID had “not only augmented and improved upon spreadsheets” but also built a stand-alone, defensible business.
2020-08-29 00:00:00 Read the full story…
Weighted Interest Score: 2.3444, Raw Interest Score: 1.2530,
Positive Sentiment: 0.4042, Negative Sentiment 0.0808
Growth-Mode Digital Strategy: Modernize And Transform
“Digital transformation” is overused, overhyped, and open to broad interpretation. Is transformation still relevant in a post-COVID world? I believe it is but only when mixed with a healthy dose of pragmatic digital modernization. Which strategy — modernize or transform — will depend on your firm’s digital maturity and economic situation.
My last post highlights the need for firms in survival mode to focus digital strategy on pragmatic moderniza…
2020-08-26 00:20:42-04:00 Read the full story…
Weighted Interest Score: 2.3381, Raw Interest Score: 1.6986,
Positive Sentiment: 0.1799, Negative Sentiment 0.0400
AI Holistic Adoption for Manufacturing and Operations: Program
“AI Holistic Adoption for Manufacturing and Operations” is a four-part series which focuses on the executive leadership perspective including key execution topics required for the enterprise digital transformation journey and holistic adoption of AI for manufacturing and operations organizations. Planned topics include: Value, Program, Data and Ethics. Here we address the Program.
The first article of this series described the fundamental responsibility of executive leaders to focus the enterprise Digital Transformation and AI Adoption on Value. AI Holistic Adoption drives the value focus and is the combination of addressing the needs of the people, processes, and tools associated with the AI solution. This is a critical perspective shift from the comfort of data science to the applicability of true customer engagement.
Once the executive leader has truly anchored their vision of the enterprise’s digital transformation and AI adoption in this perspective, they must enable their teams by addressing the formation of their analytics program.
2020-08-27 21:31:55+00:00 Read the full story…
Weighted Interest Score: 2.3277, Raw Interest Score: 1.1465,
Positive Sentiment: 0.1390, Negative Sentiment 0.0695
NOAA Awards Nearly $700,000 to Enterpreneurial Machine Learning Projects
In the computing sphere, the United States’ National Oceanic and Atmospheric Administration (NOAA) may be most well-known for its massive weather and climate models, which predominantly run on correspondingly massive supercomputers and clusters. With the advent of machine learning and artificial intelligence, however, lighter-weight applications are offering serious deliverables – and receiving considerable funding. Now, NOAA has announced that it is awarding grants to 21 small businesses through its latest round of Small Business Innovation Research (SBIR) program funding, including five businesses working to improve NOAA’s operations using machine learning.
The SBIR program targets the entrepreneurial sector, with NOAA explaining that “the risk and expense of conducting serious R&D efforts are often beyond the means of many small businesses” and SBIR loans – capped at $150,000 per awardee – can help those businesses to compete while promoting innovative research.
2020-08-24 00:00:00 Read the full story…
Weighted Interest Score: 2.2998, Raw Interest Score: 1.3944,
Positive Sentiment: 0.2490, Negative Sentiment 0.1494
A bankers guide to AI Part 5. What are the third-party dependencies? How will this technology affect my operational resiliency?
This is the final in a 5 part series (published weekly) written by guest author Amber Sutherland a banker who understands technology who currently works for Silent Eight an AI-based name, entity and transaction adjudication solution provider to financial institutions. Click here for Index and Part 1.
Operational resiliency and third party due diligence have become a significant focus in the industry and can be a barrier to doing business. Many r…
2020-08-26 00:00:00 Read the full story…
Weighted Interest Score: 2.2771, Raw Interest Score: 1.1639,
Positive Sentiment: 0.0970, Negative Sentiment 0.2425
Check out the pitch deck marketing analytics startup SuperMetrics used to win investment from Twitter and Slack backer IVP
Marketing data analytics startup Supermetrics just raised close to $50 million in a funding round backed by Twitter and Snap investor IVP and Huel investor Highland Europe.
Set up to help marketers monitor and react to campaign success, Supermetrics is part of a growing data analytics market predicted to be worth around $40 billion by 2023.
We got an exclusive look at the pitch deck the firm used to bring investors on …
2020-08-30 00:00:00 Read the full story…
Weighted Interest Score: 2.2517, Raw Interest Score: 1.3279,
Positive Sentiment: 0.2309, Negative Sentiment 0.0577
Karnataka Govt. Launches AI-Driven Movable Hospitals To Treat COVID-19 Patients
Karnataka Government recently announced the launch of AI-driven movable hospital to treat COVID-19 patients. It has been done in an effort to contain the spread of the virus in the state.
Called the Vevra Pods, these are movable capsules that are infused with artificial intelligence to prevent the spread of contagious diseases such as COVID-19, flu, TB and more. Dr Sudhakar K, the education minister tweeted that AI has the potential to transform healthcare and urged tech startups to focus on low-cost solutions.
Happy to e-launch Healthcare Pods developed by Vevra. These pods are innovative movable hospitals integrated with AI and helps in containment of contagious diseases….
2020-08-25 05:58:19+00:00 Read the full story…
Weighted Interest Score: 2.2514, Raw Interest Score: 1.2676,
Positive Sentiment: 0.1878, Negative Sentiment 0.0469
Pandemic Presents Opportunities for Robots; Teaching Them is a Challenge
The pandemic is opening up opportunities for robots in the restaurant business as kitchens look for ways to distance employees and customers.
White Castle, the regional hamburger restaurant chain, is testing a robot arm from Miso Robotics, for cooking french fries and other food, according to an account in the Associated Press. The two companies had been in discussions for about a year; talks picked up when the coronavirus hit. One potential benefit is the robot can free up time for human staff to handle increasing delivery orders.
Robot use by the restaurant industry is expected to pick up. “I expect in the next two years you will see pretty significant robotic adoption in the food space because of Covid,” stated Vipin Jain, the co-founder and CEO of Blendid, a Silicon Valley startup.
Blendid’s robot kiosk makes fresh smoothies according to a recipe customers tweak from their smartphone app. A Blendid employee keeps ingredients refilled once or twice a day. The company has a handful of kiosks operating around San Francisco, and is making sales outreaches to hospitals, shopping malls and supermarkets. “What used to be forward-thinking—last year, pre-Covid—has become current thinking,” Jain said.
2020-08-27 21:47:10+00:00 Read the full story…
Weighted Interest Score: 2.1839, Raw Interest Score: 1.4908,
Positive Sentiment: 0.2214, Negative Sentiment 0.0738
BBVA builds gender-neutral global chatbot
BBVA has kicked against the trend to assign female voices to artificial intelligence assistants with the launch of Blue, a gender-neutral chatbot trained to answer customer’s everyday banking queries.
A recent Unesco report analyses the role of education in helping to remedy gender bias in technology. The United Nations entity maintains that virtual assistants’ feminine nature and the subservience they express is a clear example of how technology contributes to the perpetuation …
2020-08-28 11:29:00 Read the full story…
Weighted Interest Score: 2.1615, Raw Interest Score: 1.7075,
Positive Sentiment: 0.0569, Negative Sentiment 0.2846
Deep Learning DevCon 2020: Association of Data Scientists Launches It’s Latest Virtual Conference
The Association of Data Scientists (ADaSci), the premier global professional body of data science & machine learning professionals, announces the launch of Deep Learning DevCon 2020 (DLDC).
The Deep Learning conference of the year, DLDC is a leading virtual conference exclusively for deep learning practitioners across the world. The 2-day virtual conference will be held on 29th and 30th October bringing influential people in the deep learning domain on a single platform.
Over the years, deep learning has become a crucial subarea of the artificial intelligence and machine learning domain with many exciting use cases that have been explored in various industries. Deep learning models are dominating in a variety of applications and have outperformed the classical machine learning models in many ways.
2020-08-26 05:40:18+00:00 Read the full story…
Weighted Interest Score: 2.1217, Raw Interest Score: 1.9269,
Positive Sentiment: 0.3613, Negative Sentiment 0.0401
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