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By 2023, 75% Firms to Hire AI Behaviour Forensic Experts

While the number of organisations hiring ML forensic and ethics investigators remains small today, that number will accelerate in the next five years

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AI
This is the much talked about turf of Artificial Intelligence that now even handles the preliminary part of 'action' that was needed in response to a 'trigger'. Pixabay

By 2023, seventy five per cent of large organisations would hire artificial intelligence (AI) behaviour forensic, privacy and customer trust specialists to reduce brand and reputation risk, Gartner Inc. predicted on Thursday.

Bias based on race, gender, age or location, and bias based on a specific structure of data, have been long-standing risks in training AI models.

“New tools and skills are needed to help organisations identify these and other potential sources of bias, build more trust in using AI models, and reduce corporate brand and reputation risk. More and more data and analytics leaders and chief data officers (CDOs) are hiring ML (machine learning) forensic and ethics investigators,” Jim Hare, Research Vice President at Gartner, said in a statement.

artificial intelligence, nobel prize
“Artificial intelligence is now one of the fastest-growing areas in all of science and one of the most talked-about topics in society.” VOA

Increasingly, sectors like finance and technology are deploying combinations of AI governance and risk management tools and techniques to manage reputation and security risks.

In addition, organisations such as Facebook, Google, Bank of America, MassMutual and NASA are hiring, or have already appointed, AI behaviour forensic specialists who primarily focus on uncovering undesired bias in AI models before they are deployed.

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“While the number of organisations hiring ML forensic and ethics investigators remains small today, that number will accelerate in the next five years,” added Hare. (IANS)

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IIT-Madras Develops AI Model for Learning and to Solve Engineering Problems

The researchers utilised a data-driven AI and a deep learning model to arrive at solutions for engineering problems after training the AI with data sets

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IIT-Madras
The researchers of IIT-Madras are going to establish a start-up to deploy their AI Software called 'AISoft' to develop solutions to engineering problems in varied fields. Pixabay

The Indian Institute of Technology, IIT-Madras on Monday said its researchers have developed algorithms that enable novel applications for artificial intelligence (AI), machine learning and deep learning to solve engineering problems.

The researchers are going to establish a start-up to deploy their AI Software called ‘AISoft’ to develop solutions to engineering problems in varied fields such as in thermal management, semiconductors, automobile, aerospace and electronic cooling applications.

“We tested AIsoft and used it to solve such thermal management problems. We found it to be nearly million-fold faster compared to existing solutions currently used in the field,” said Vishal Nandigana, Assistant Professor, Fluid Systems Laboratory, Department of Mechanical Engineering.

“Our AI works on any generalised rectilinear and curvilinear input geometry. Our research saves the computational time, which is the bottleneck to solve most engineering problems, Nandigana added.

The researchers utilised a data-driven AI and a deep learning model to arrive at solutions for engineering problems after training the AI with data sets.

These prior data sets can be from existing big data in the relevant engineering industry where there are lots of experimental data available.

IIT-Madras
IIT-Madras on Monday said its researchers have developed algorithms that enable novel applications for artificial intelligence (AI), machine learning and deep learning to solve engineering problems. Pixabay

Also, if data is not available for training the AI, it can be generated using commercially-available CFD (Computational Fluid Dynamics) software on small independent pieces of the full-blown problem.

This idea is new and is only now being looked at by a few research groups across the world. Most of these research groups use Convolutional Neural Networks (CNN) or C-GAN (conditional generative adversarial network) to solve engineering problems.

They have also developed hardware products using graphics processing unit (GPU) and multi-threading processing to solve thermal management problems in thermal and electronic cooling industries.

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Both the software and hardware products are several times faster than commercial numerical method software and open source software in the market.

These algorithms will solve a lot of pressing problems for industries and can also be used for educational purposes. (IANS)