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AI-Based Solutions May Pose Risk to Business

These deficiencies could undermine the decisions, predictions, or analysis AI applications produce, subjecting the company to competitive harm, legal liability, and brand or reputational harm

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AI scenarios present ethical issues ranging from privacy, human rights, employment or other social issues.
AI scenarios present ethical issues ranging from privacy, human rights, employment or other social issues. Pixabay
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In a message to investors, Microsoft has said that the challenges to adoption of Artificial Intelligence (AI)-based solutions by customers as well as changes to trade policy or agreements as a result of populism, protectionism or economic nationalism may pose a risk to its businesses.

In a regulatory filing on Friday, Microsoft said that the ability to convert data into AI drives its competitive advantage, but issues in the use of AI in its offerings may result in reputational harm or liability.

The Redmond, Washington-headquartered tech giant, which does business in 200 countries, said inappropriate or controversial data practices by Microsoft or others could impair the acceptance of AI solutions.

Microsoft REConsiders Using AI, Pixabay
Microsoft REConsiders Using AI, Pixabay

These deficiencies could undermine the decisions, predictions, or analysis AI applications produce, subjecting the company to competitive harm, legal liability, and brand or reputational harm.

It also pointed out that some AI scenarios present ethical issues ranging from privacy, human rights, employment or other social issues.

Also Read: Microsoft Will Become an AI First Industry, Will Improve Lives, Says CEO

Moreover, the proliferation of social media may increase the likelihood, speed, and magnitude of negative brand events, it added.

The tech giant also warned how emerging nationalist trends in specific countries may significantly alter the global trade environment. (IANS)

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Researchers Develop AI That Can Help Make Cancer Treatment Less Toxic

The new "self-learning" machine-learning technique could make the dosing regimen less toxic but still effective

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Cancer
Indian-American researchers unleash turmeric's power to fight cancer. Pixabay

MIT researchers, including one of Indian-origin, have developed novel machine-learning techniques to improve the quality of life for patients by reducing toxic chemotherapy and radiotherapy dosing for an aggressive form of brain cancer.

Glioblastoma is a malignant tumour that appears in the brain or spinal cord, and the prognosis for adults is no more than five years.

Patients are generally administered maximum safe drug doses to shrink the tumour as much as possible, but they still remain at risk of debilitating side effects.

The new “self-learning” machine-learning technique could make the dosing regimen less toxic but still effective.

It looks at the treatment regimen currently in use, and finds an optimal treatment plan, with the lowest possible potency and frequency of doses that should still reduce tumour sizes to a degree comparable to that of traditional regimen, the researchers said.

“We kept the goal where we have to help patients by reducing tumour sizes but, at the same time, we want to make sure the quality of life — the dosing toxicity — doesn’t lead to overwhelming sickness and harmful side effects,” said Pratik Shah, principal investigator from the Massachusetts Institute of Technology (MIT) in Boston, US.

Cancer
Representational image. Pixabay

The findings will be presented at the 2018 Machine Learning for Healthcare conference at Stanford University in California, US.

In simulated trials of 50 patients, the model comprising of artificially intelligent “agents”, designed treatment cycles that reduced the potency to a quarter or half of nearly all the doses while maintaining the same tumour-shrinking potential.

Many times, it skipped doses altogether, scheduling administrations only twice a year instead of monthly.

You May Also Like to Read About The Relation of Cancer Cells With Immune System- Decoded: How Cancer Cells Cripple Immune System

However, the researchers also had to make sure the model does not just dish out a maximum number and potency of doses. Whenever the model chooses to administer all full doses, it gets penalized, so instead it chooses fewer, smaller doses.

“If all we want to do is reduce the mean tumour diameter, and let it take whatever actions it wants, it will administer drugs irresponsibly,” Shah said.

“Instead, we need to reduce the harmful actions it takes to get to that outcome.” (IANS)