Hundreds of AI tools have been built to catch covid. None of them helped.


With data coming out of China, which had a four-month head start in the race to beat the pandemic. If machine-learning algorithms could be trained on that data to help doctors understand what they were seeing and make decisions, it just might save lives. “I thought, ‘If there’s any time that AI could prove its usefulness, it’s now,’” says Wynants. “I had my hopes up.” It never happened—but not for lack of effort.

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Coralogix raises $55 Million in series-C funding led by Greenfield Partners


Israel-based leading provider of machine learning powered log analytics and monitoring solutions, Coralogix announced it has raised $55 million in a Series C funding round led by Greenfield Partners, bringing the company’s total amount raised to $96 million. The company aims to pursue its strategic 5-year growth plan for the India market, whilst assisting companies with regional server support, data storage capabilities and compliance with the country’s data privacy laws.

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PayPal’s new ‘super app’ is ready to launch, will also include messaging


PayPal’s plan to morph itself into a “super app” have been given a go for launch. According to PayPal CEO Dan Schulman, speaking to investors during this week’s second-quarter earnings, the initial version of PayPal’s new consumer digital wallet app is now “code complete” and the company is preparing to slowly ramp up. Over the next several months, PayPal expects to be fully ramped in the U.S., with new payment services, financial services, commerce and shopping tools arriving every quarter.

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“Above the Trend Line” – Your Industry Rumor Central for 7/29/2021

Above the Trend Line

News items grouped by category such as M&A activity, people movements, funding news, industry partnerships, customer wins, rumors and general scuttlebutt floating around the big data, data science and machine learning industries including behind-the-scenes anecdotes and curious buzz.

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Continuous Testing for Machine Learning Systems


Validate the correctness and performance of machine learning systems through the ML product lifecycle.Photo by Tolga Ulkan on UnsplashTesting in the software industry is a well-researched and established area. The good practices which have been learned from the countless number of the failed projects help us to release frequently and have fewer opportunities to see defects in production. Industry common practices like CI, test coverage, and TDD are well adopted and tailored for every single project.However, when we try to borrow the SWE testing philosophy to machine learning areas, we have to solve some unique issues. In this post, we’ll cover some common problems in the testing of ML models (systems) and discuss potential solutions.The ML system here stands for a system (pipeline) that generates prediction (insights) which can be consumed by users. It may include a few machine learning models. For example, an OCR model(system) could include one ML model to detect text region, one ML model to tell which current text region class is ( car plate vs road sign), and one model to recognize the text from a picture.A model is composed of the code (algorithm, pre-process, post-process, etc), data, and infrastructure which facilitates the runtime.The scope of ML system testing, Image by authorDifferent types of testings cover the quality assurance for different components of the system.Data testing: ensuring new data satisfies your assumptions. This testing is needed before we train a model and make predictions. Before training the model, the X and y (labels)Pipeline testing: ensuring your pipeline is set up correctly. It’s like the integration tests in SWE. For the ML system, it may measure consistency (reproducibility) as well.Model evaluation: evaluating how good your ML pipeline is. Depends on the metrics and dataset set you’re using, it could refer to different things.Evaluation on holdout/cross-validation dataset.Evaluation of deployed pipelines and ground truth(continuous evaluation ).Evaluation based on the feedback of system users (the business-related metrics, not a measurable ML proxy)There are a bunch of techniques that can be applied in the process, like slice-based evaluation, MVP(a critical subset of data) groups/samples analysis, ablation study, user subgroup-based experiments (like Beta testing, and A/B testing).Model testing: involves explicit checks for behaviors that we expect our model to follow. This type of testing is not for telling us the accuracy-related indicators, but for preventing us from behaving badly in production. Common test types include, but are not limited to:Invariance(Perturbation) Tests: perturbations of the input without affecting the model’s output.Directional Expectation Tests: to achieve we should have a predictable effect on the model output. For example, if the loss of blood within a surgery goes up, the blood for transfusion should go up as well.Benchmark regression: use predefined samples and accuracy gate to ensure a version of the model won’t introduce insane issues.When do we perform tests? Image by authorSome people may ask why we need to use holdout evaluation and continuous evaluation to measure almost the same metrics in CI and serving time.One reason is that we can’t fully estimate model performance by seeing metrics on a predefined holdout dataset is that the data leakage sometimes is hard to detect than it looks. For example, some features which were expected to exist in the serving time turn out to have high latency to acquire, so our trained models can’t get used to seeing this feature always being empty.Sometimes model evaluation could be very expensive, so a full cycle holdout evaluation is not feasible integrated into CI. In this case, we can define a subset regression evaluation within the CI, and only do the full evaluation before important milestones.Model testing is not a one-off step, instead, it should be a continuously integrated process with the automation setup. Some of the test cases can be performed with the CI process, so each code commit will trigger them and we can guarantee the code/model quality in the main branch of a repo. Others can be conducted in the serving environment, so we won’t be blind to how well our system performs, and we can have relatively sufficient time to fix issues when we have them. Sometimes the ongoing tests within the serving environment can be seen as a part of the monitoring component, and we can integrate with alerting tool to close the loop.Machine learning systems are not straightforward to test, not only because it includes more components (code + data) to verify, but also it has a dynamic nature. Although we didn’t change anything our models can be stale because of the data change (data drift) or the nature of things change (concept drift) over time.Automated testing is an essential component in CI / CD to verify the correctness of pipelines with a low footprint. While manual tests and human-in-the-loop verification are still crucial steps before we say a new ML pipeline is production-ready. After a pipeline has been released to production, continuous monitoring and evaluation can ensure we’re not flying blind. Finally, customer feedback-based tests (.i.e A/B tests) are able to tell us if the problem we are trying to solve is actually getting better.There is no silver bullet in the ML system testing, continuously trying to cover edge cases would help us have fewer opportunities to make mistakes. Hope one day we can figure out a simple metric like code coverage to tell if our system is good enough.

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IBM launches AI Starter Kit to deploy ML Models faster (with Video)


IBM recently launched a new machine learning, end-to-end pipeline starter kit to help developers and data scientists to build machine learning applications and deploy them quickly in a cloud-native environment. The starter kit is part of the IBM Cloud-Native Toolkit–an open-source collection of assets that provide an environment for developing cloud-native applications for deployment within Kubernetes and Red Hat OpenShift. Assets created with the Cloud-Native Toolkit can be deployed in any cloud or hybrid cloud environment. 

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AI Edge Chip vendor Blaize Gets $71M in Series D funding to expand its Edge, automotive products


Seven months ago, AI edge chip vendor, Blaize, launched a new no-code AI software application to make it easier for customers to build applications for its AI chip-equipped PCIe cards, modules and devices. Now, the company has announced the receipt of an additional $71 million in Series D funding from investors including Franklin Templeton, Temasek and DENSO to continue its product development, engineering and sales efforts.

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OpenAI releases Triton, a programming language for AI workload optimization

Open AI

OpenAI today released Triton, an open source, Python-like programming language that enables researchers to write highly efficient GPU code for AI workloads. Triton makes it possible to reach peak hardware performance with relatively little effort, OpenAI claims, producing code on par with what an expert could achieve in as few as 25 lines.

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[Paper Summary] DeepMind Researchers introduce Epistemic Neural Networks (ENNs) for Uncertainty Modeling in Deep Learning

Deep learning algorithms are widely used in numerous AI applications because of their flexibility and computational scalability, making them suitable for complex applications. However, most deep learning methods today neglect epistemic uncertainty related to knowledge which is crucial for safe and fair AI. A new DeepMind study has provided a way for quantifying epistemic uncertainty, along with new perspectives on existing methods, all to improve our statistical knowledge of deep learning.

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Gupshup nabs $240M to power messaging channels


Conversational messaging platform Gupshup today announced that it raised $240 million led by Tiger Global Management, with participation from Fidelity Management, Think Investments, Malabar Investments, Harbor Spring Capital, and others. The tranche, which values the company at $1.64 billion, will be used to build new tools, infrastructure, and services while expanding Gupshup’s global reach, CEO Beerud Sheth said.

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[Book Review] Analytics of Life by Mert Damlapinar

Analytics of Life: Making Sense of Data Analytics, Machine Learning & Artificial Intelligence is a work of non-fiction in the business and technology sub-genres, and was penned by author Mert Damlapinar. As the title and subtitle suggest, the subject matter of the work concerns the growing field and practices of data analytics.

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[Paper] MIT & Google Quantum Algorithm trains wide and deep Neural Networks

Quantum algorithms for training wide and classical neural networks have become one of the most promising research areas for quantum computer applications. While neural networks have achieved state-of-the-art results across many benchmark tasks, existing quantum neural networks have yet to clearly demonstrate quantum speedups for tasks involving classical datasets. Given deep learning’s ever-rising computational requirements, the use of quantum computers to efficiently train deep neural networks is a research field that could greatly benefit from further exploration.

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Noetic Cyber raises $20M to automate cybersecurity remediation

Noetic Cyber

Noetic Cyber, a startup creating a platform that leverages automation to identify cyber threats, today emerged from stealth with $20 million, including $15 million in series A funding from Energy Impact Partners, TenEleven Ventures, and Glasswing Ventures. Cofounder and CEO Paul Ayers says that the funds will be used to scale up Noetic’s operations and go-to-market capabilities, allowing the team to grow particularly on the sales and marketing side.

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AI for sustainable Value Chains

Irrigation terrace

Faced with the rise of these new expectations, companies have higher demands from their suppliers and are improving the sustainability of their value chains. However, the required transformation does not stop there, it is now necessary to ensure the quality of the information that circulates in the supply chains and empower each actor with suitable technological tools to manage it. To take full advantage of artificial intelligence (AI) and its capabilities, large amounts of data are necessary. As value chain data is too often scattered, a system integrating supply chain data collection and consolidation is needed.

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Aspect based sentiment analysis on financial news data using classical machine learning algorithms

Sentiment analysis is a very popular technique in Natural Language Processing. We can see it applied to get the polarity of social network posts, movie reviews, or even books. However basic sentiment analysis can be limited, as we lack precision in the evoked subject. Let’s take the example of reviews for a computer: how do we know what is good/bad ? Is it the keyboard, the screen, the processor?

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Top 5 uses of AI in Telecommunications

AI in telecom

Companies in the telecommunications (telecom) industry are benefiting greatly from artificial intelligence (Ai) and the practical use of Ai through machine learning. For example, Gartner research indicates that product leaders must understand the realistic adoption and potential impact of strong Ai technologies across the telecom industry. What are some of the best ways Ai can help the telecom industry? Let’s take a look at the top 5 uses and how they can help your company succeed.

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InVia Robotics raises $30M for warehouse robotics push

InViva Robotics

InVia Robotics, an industrial robotics company based in Los Angeles, California, today announced that it raised $30 million in series C financing co-led by Microsoft’s M12 Ventures and Qualcomm, with participation from Hitachi. InVia says that the new tranche of equity funding will be used to support its growth, specifically through adopting Qualcomm’s Robotics RB5 Platform and drawing on AI expertise from Hitachi and Microsoft.

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Orum raises $25M to automate outbound sales workflows


Sales support platform Orum today announced that it closed a $25 million series A funding round led by Craft Ventures, with participation from several existing backers. The funding brings the company’s total raised to $29 million at a $125 million valuation, and cofounder and CEO Jason Dorfman says the round will be put toward general expansion, mostly focused on product, customer service, and international market expansion.

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5 questions to help you choose the right Data Labeling Tool

Data labeling tool

The versatility of a data labeling tool can make or break your data quality. And the data quality can make or break your algorithms. And what happens when our algorithms misinterpret or fail? — Karthik Vasudevan, Founder at Traindata Inc. This post will guide you to ask five questions to help you choose the best data labeling tool.

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The future of Artificial Intelligence in Weather Forecasting

Weather station

Today’s weather forecasts are generated by some of the world’s most sophisticated computers. As you may know, weather forecasts are very unpredictable. This is because the climate is a very complex and volatile phenomenon that requires a great amount of money, data, and time to evaluate. The future may follow a very different path regarding weather forecasting: and that future is A.I.

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