Following months of inner conflict and opposition from Congress and thousands of Google employees, Google today announced that it will reorganize its AI ethics operations and place them in the hands of VP Marian Croak, who will lead a new responsible AI research and engineering center for expertise.
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This month, Shell, Microsoft, C3.ai, and Baker Hughes announced the launch of their collaborative AI initiative, Open AI Energy Initiative™ (OAI). Designed for the process and energy sectors, OAI is an open-source tool that offers AI-based solutions, diagnostics, and monitoring to prevent crucial issues.
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AI research has also accelerated in the last 5 years with more research papers published in 2020… Funding of AI research has also accelerated with Congress approving AI related initiatives such as boosting funding in institutions of higher learning. The Biden administration² is currently working on an AI policy that will see the United States continue investing more in AI research, military applications and national security. The current AI funding¹ by the federal government is a good start but more needs to be done.
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Rule-based AI systems borrow from rule-based expert system development, which tapped the knowledge of human experts to solve complex problems by reasoning through bodies of knowledge. Expert systems emerged in the 1970s and 1980s. Today rule-based AI models include a set of rules and a set of facts, described in a recent account in BecomingHuman/ Medium. “You can develop a basic AI model with the help of these two components,” the article states.
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As AI becomes more mainstreamed as a software development approach, enterprise IT operations need to get involved with managing its complexity. The need for AI to help IT operations, AIOps, has accelerated as organizations attempt to incorporate AI systems into their production environments. Tools positioning in the AIOps market incorporate analytics and machine learning to help get the job done. The use of tools in this category is projected by Gartner to grow from five percent of large enterprises in 2018 to 30% by 2023.
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In our last post we took a broad look at model observability and the role it serves in the machine learning workflow. This leads us to a natural question of: what should I monitor in production? The answer, of course, depends on what can go wrong. In this article we will be providing some more concrete examples of potential failure modes along with the most common symptoms that they exhibit in your production model’s performance.
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We misclassify a lot of things, all the time, daily, and at any moment. You are waiting in a restaurant for a friend to come and have lunch with you. Your eyes are scanning the people that are entering the busy eatery. Assume that it is a cold day and raining or snowing, which means that most of those coming into the restaurant are wearing heavy clothes and generally covered up. It would be quite easy to spot someone that appeared to be your friend, based perhaps on their height and overall shape, yet once they removed their coat and hat, presumably by now seeing clearly the face of the person, you would realize it is not the person you were waiting for.
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Approaching (Almost) Any Machine Learning Problem. There are a growing number of works out there addressing how to approach machine learning problems, many of them quite good. But how many of them are written by a 4x Kaggle Grandmaster?
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I’m sure many of us are curious about the mathematics behind such algorithms — how does mathematics factor into these algorithms, and how can the manipulation of mathematical systems produce such stunning results on par with detecting COVID-19?
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Smoothing or averaging is a form of noise reduction and traders use to get a clearer picture on the trend or on extreme moves. The Stochastic Oscillator is known to have a signal line called the %D where a simple moving average is applied to the Stochastic formula so that it smoothes it out. In this article, we will create a Stochastic Oscillator entirely made out of exponential moving averages.
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SaaS solutions have been gaining popularity over recent years to the point where most software products are using SaaS based model. SaaS has been widely accepted by industry as it requires little to no installation, software can be instantly dispatched via cloud and cloud computing offers flexibility in computing power and resources.
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Reprise, a software demo platform for enterprise sales and marketing teams, today announced that it has raised $17 million in series A funding led by Bain Capital Ventures. The company says that the proceeds, which follow on a $3 million seed investment, will be put toward expanding Reprise’s platform and hiring new employees.
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Podcasts are a great way to learn about novel fields and tools, as well as keeping yourself updated with the fields that you care about. I also believe that podcasts which are mainly centered around interviews are a great way to learn about the rockstars and superheroes of the AI world. You get a glimpse of how they think, what are they working on, and they solved a particular problem. In this post, I am not going into the details of why I think podcasts are great and Machine Learning learners and practitioners should listen to them.
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The whole idea of robots in hospitals is not new for healthcare professionals. The idea of using robotic technologies in healthcare evolved in 1985. It started when healthcare planned to transform robots into precise machinery for surgery. “The idea came into reality in early 2000 with the invention of the DaVinci robot. Hospital management software and robotic technologies are proof of how technologies have evolved.” According to research conducted by Credence, the global market of medical robotics will grow to USD 20 billion by 2023.
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Application performance monitoring startup Sentry today announced it has secured $60 million in series D financing for a post-money valuation of $1 billion. Sentry says the funds will fuel product development and go-to-market functions, as well as hiring across the company’s San Francisco, Toronto, and Vienna offices.
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Bosch has partnered with Cambridge-based AI blockchain startup Fetch.ai with the aim of transforming existing digital ecosystems using distributed ledger technologies (DLTs). The global engineering giant will test key features of Fetch.ai’s testnet until the end of this month and will deploy a node on the network. The strategic engineering project between Fetch.ai and Bosch…
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Artificial Intelligence (AI) is here to stay, over a decade it has been changing the industries on a very accelerated pace and despite many people, even in technology area, still been skeptical about it and some simply do not like it, there is no way back and now it is time to master it and operate AI/ML with same level of maturity industry have for software development, automating it and integrating it with all IT Eco-Systems — this is the MLOps Turn.
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Researchers at MIT have created an algorithm-based architecture called SpAtten that reduces attention computation and memory access in natural language processing (NLP) systems. If we think it’s hard to learn a new language, imagine the challenges hardware and software engineers face when using CPUs and GPUs to process extensive language data. Natural language processing (NLP) attempts to bridge this gap between language and computing.
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AI-powered math tutoring app Photomath today announced that it raised $23 million in series B funding. The company says the proceeds will be used to grow headcount, invest in AI, and scale both product and marketing development. An estimated 65% of households in the U.S. with children report using online learning during the pandemic, with 11% reporting having no live contact with a teacher in the past week (as of November 2020).
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When I was in graduate school in the 1990s, one of my favorite classes was neural networks. Back then, we didn’t have access to TensorFlow, PyTorch, or Keras; we programmed neurons, neural networks, and learning algorithms by hand with the formulas from textbooks. We didn’t have access to cloud computing, and we coded sequential experiments that often ran overnight. There weren’t platforms like Alteryx, Dataiku, SageMaker, or SAS to enable a machine learning proof of concept or manage the end-to-end MLops lifecycles.
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