Machine Learning in the Automotive Industry

automobiles & ML

AI simulates the applications that extend the automotive manufacturing ground. Automakers can use #AI-driven systems to create directories and manage workflows, enabling the robots to operate safely alongside humans on manufacturer grounds and panel lines and identify defects in segments going into cars and trucks.

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Algorithms are not Sexist — We are!


The AI that famously created an image of AOC in a bikini was behaving inappropriately but rationally. If you believe the AI that created male and female images from pictures of their heads, men typically wear suits, while women prefer low-cut tops and bikinis. It’s been reported in the press that this is evidence that the future of AI is sexist. The argument being that because the internet is awash with pictures of scantily-clad females, AI will assume that this is normal. A fair point? Not necessarily…

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Federated Learning: A decentralized form of Machine Learning

Most major consumer tech companies that are focused on AI and machine learning now use federated learning – a form of machine learning that trains algorithms on devices distributed across a network, without the need for data to leave each device. Given the increasing awareness of privacy issues, federated learning could become the preferred method of machine learning for use cases that use sensitive data (such as location, financial, or health data).

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Texas A&M Reinforcement Learning Algorithm automates Oil and Gas reserve forecasting

Oil and Gas

Oil and gas extraction is a messy business, not least because much of the initial discovery process relies on educated guesswork that often proves fruitless. O&G companies are always searching for ways to reduce these false positives and thereby the costs of their discovery operations – and now, a team of researchers at Texas A&M University have produced a new algorithm that automates prediction of oil and gas reserves.

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The 10 Algorithms Machine Learning engineers need to know

A data scientist or a machine learning enthusiast allow students of top private engineering colleges in Rajasthan to use these techniques to create functional Machine Learning projects. There are certain types of Machine Learning techniques including supervised learning, unsupervised learning, and reinforcement learning. All these techniques are used in this list of common Machine Learning Algorithms. Some of them are as follows:

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Bias in Machine Learning Algorithms

The progress made in the field of machine learning and its capabilities in solving practical problems have heralded a new era in the broader domains of artificial intelligence (AI) and technology. Machine learning algorithms can now identify groups of cancerous cells in radiographs, write persuasive advertising copy and ensure the safety of self-driving cars.

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AI: The Algorithmic Doctor

Ever wondered what all it takes for machines to act like humans. AI functioning as a doctor is much more technical than it looks, so here we are with our latest blog on algorithms used for AI in Psychiatry. Do give it a read to know what goes behind to get the unmatched accuracy and advanced features for supporting mental health!

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Use cases, Algorithms, tools, and example implementations of Machine Learning in Supply Chain

Supply chain

Compared to machines, humans are slow and inconsistent. Managing the end-to-end process of a delivery system from acquiring data, managing data, understanding it and making decisions, can be difficult and tiring. Demand is uncertain, supply is at risk, competition is intense. Supply chain (SC) excellence often relies on the organisation’s ability to incorporate the end-to-end processes of getting materials or components, assembling them into products, and delivering them to the customers. 

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AI software tool helping Quantum Computers to ‘Self-Tune’ for improved performance

Quantum computing

With the overriding goal of reducing hardware errors and instability caused by environment noise, quantum computing software startup Q-CTRL has unveiled an AI-based toolset billed as enabling quantum computers to “self-tune” for noise and error suppression.

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Machine Learning – it’s all about assumptions

Machine Learning – it’s all about assumptions. There are tons of Data Science enthusiasts who are either looking for a job in Data Science or switching jobs for better opportunities. Every one of these individuals must to go through some strict hiring process containing several rounds of interviews. There are several basic questions which a recruiter/interviewer expects to be answered. Knowing about the assumptions of the popular Machine Learning Algorithms along with their Pros & Cons is one of them.

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My machine learning model does not learn. What should I do?

If you work with data in general, and machine learning algorithms in particular, you might be familiar with that feeling of frustration when a model really does not want to learn the task at hand. You have tried it all, but the accuracy metric just won’t rise. What next? Where is the problem? Is this an unsolvable task or is there a solution somewhere you’re not aware of?

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Machines misbehaving: 62 scientific studies showing how Big Tech’s Algorithms can harm the public

Might it be time to create an “FDA for algorithms? In the United States, there is currently no federal institution that protects the public from harmful algorithms. We can buy eggs, get a vaccine, and drive on highways knowing there are systems in place to protect our safety: the USDA checks our eggs for salmonella, the FDA checks vaccines for safety and effectiveness, the NHTSA makes sure highway turns are smooth and gentle for high speeds. But what about when we run a Google search or look up a product on Amazon?

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How AI can learn from Genetics

Coding a solution for the 8-queens puzzle with Genetic Search Algorithms, AI algorithms that mimic how our genes are passed and expressed. James Watson and Francis Crick first published their groundbreaking study on the double-helix form of deoxyribonucleic acid (DNA) and how it expresses our genes, which up until then, were mere abstractions lacking a concrete, biological, definition.

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Algorithmic Trading Models — Cyclical Methods

Device trading app

Typically, these models are likely to be most effective around fluctuating or periodic instruments, such as forex pairs or commodities, which is what I have backtested them on. The aim behind each of these models is that they should be objective and systematic i.e. we should be able to translate them into a trading bot that will check some conditions at the start of each time period and make a decision if a buy or sell order should be posted or whether an already open trade should be closed.

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