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USC researchers enable AI to use its "imagination." - USC Viterbi

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The new AI system takes its inspiration from humans: when a human sees a color from one object, we can easily apply it to any other object by substituting the original color with the new one. Now, imagine the same cat, but with coal-black fur. Now, imagine the cat strutting along the Great Wall of China. Doing this, a quick series of neuron activations in your brain will come up with variations of the picture presented, based on your previous knowledge of the world. In other words, as humans, it's easy to envision an object with different attributes.


Is GitHub Copilot worth the cockpit?

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One month ago, GitHub announced its latest, shiny product: an artificial intelligence tool developed by GitHub and OpenAI to assist users of Visual Studio Code by autocompleting code, but on the next level. It's a machine learning-powered software that can write code by itself, generating quite impressive programming functions. Here is the catch, there are a lot of dislikes going on for this cute little fellow. And I don't get why so much hate for this, how can you not like something like this in the market, and that too for free. Let's address them one by one So many articles are flooding to criticize, just for the sake of writing something.


A Machine Learning Approach for Tracing Tumor Original Sites With Gene Expression Profiles – Digital Health and Patient Safety Platform

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Some carcinomas show that one or more metastatic sites appear with unknown origins. The identification of primary or metastatic tumor tissues is crucial for physicians to develop precise treatment plans for patients. With unknown primary origin sites, it is challenging to design specific plans for patients. Usually, those patients receive broad-spectrum chemotherapy, while still having poor prognosis though. Machine learning has been widely used and already achieved significant advantages in clinical practices.


Artificial Intelligence and Human Brain

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Intelligence, in simpler words, can be explained as the mental ability of reasoning, problem-solving, and learning. Intelligence comes with perception, attention, and planning. Humans are the only resource of intelligence on this planet and this is what makes us stand out from all the natural god-gifted resources on this planet. The human brain has the capability of making decisions, remembering things of the past, and calculating for the future. Artificial intelligence as the name itself suggests it is a man-made intelligent machine.


What Should be Done to Make the Best of Data?

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Every possible organization that one can think of relies on data to achieve the set objectives. On that note, having access to data that isn't smart enough to get goals accomplished poses a hurdle. It is thus important to have data that is transformed in a manner that can cater to the needs and objectives of the organization. With most organizations relying on Artificial Intelligence (AI) and machine learning, the necessity of dealing with the right data is all the more important for the sole reason that the models employed aim at obtaining meaningful insights. No wonder data is vast and one shouldn't ideally fall short of it while aiming at the objectives.


Future Says... Ethical AI

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"AI is an instrument just like anything else. You can do harm and you can do wonderful things. ESG is the embodiment of all the good things you can do with AI. Squeeze all the juice out of AI but at the same time we need to understand the consequences so we can do things responsibly!" The wise words from Aiko Yamashita, Senior Data Scientist at the Advanced Analytics Centre of Excellence in DNB Bank, during our conversation on Altair's'Future Says'.


Restaurant Revenue Prediction

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Restaurants are an essential part of a country's economy and society. Whether it may be for social gatherings or a quick bite, most of us have experienced at least one visit. With the recent rise in pop up restaurants and food trucks, it's imperative for the business owner to figure out when and where to open new restaurants since it takes up a lot of time, effort, and capital to do so. This brings up the problem of finding the best optimal time and place to open a new restaurant. TFI which owns many giant restaurant chains has provided demographic, real estate, and commercial data in their restaurant revenue prediction on Kaggle.


How to Start a Career in Artificial Intelligence and Machine Learning?

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Are you planning to start a career in Artificial Intelligence and Machine learning? Well, then this article is for you. Building a career in AI and ML is not easy nor hard either. But it requires a dedicated approach. Sometimes when you're from an IT background, you may feel like swapping the career options too, because of the diverse opportunities.


A deeper look into the impact of new technologies on our work

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But before delving into'behind-the-scenes' of US banking industry meeting ATM, let's turn back time for a second -- on March 27th, 1998, in the New Tech 1998 conference in Denver, Colorado. Here, Neil Postman, a prominent American cultural critic and professor at New York University, gave a keynote lecture. Professor Postman has been a long-time scholar of how new technologies relate to human society, and the book'Amusing Ourselves to Death', a 1985 book that rose to stardom, shows how television technology is destroying public discourse and turning everything into entertainment. I think it has something to do with how we feel about the impact of today's media and how our lives exposed to it are deteriorating. Since this book, Professor Postman has strongly criticized the tendency to respond to all social problems through technical solutions.


Pinaki Laskar on LinkedIn: #MLOps #AI #machinelearning

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AI Researcher, Cognitive Technologist Inventor - AI Thinking, Think Chain Innovator - AIOT, XAI, Autonomous Cars, IIOT Founder Fisheyebox Spatial Computing Savant, Transformative Leader, Industry X.0 Practitioner Why #MLOps is the key for productionized ML system? ML model code is only a small part ( 5–10%) of a successful ML system, and the objective should be to create value by placing ML models into production. F1 score) while stakeholders focus on business metrics (e.g. Improving labelling consistency is an iterative process, so consider repeating the process until disagreements are resolved as far as possible. For instance, partial automation with a human in the loop can be an ideal design for AI-based interpretation of medical scans, with human judgement coming in for cases where prediction confidence is low.