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Microsoft To Acquire Conversational Artificial Intelligence (AI)

The acquisition of Semantic Machines in May brought a revolutionary new approach to conversational AI

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Microsoft
Microsoft launches e-commerce portal for Telangana's handloom weavers. Pixabay

Microsoft has announced to acquire a conversational Artificial Intelligence (AI) and bot development company XOXCO for an undisclosed sum.

Texas-based XOXCO has been paving the way in conversational AI since 2013 and was responsible for the creation of Howdy, the first commercially available bot for Slack that helps schedule meetings.

“It also developed Botkit which provides the development tools used by hundreds of thousands of developers on GitHub. Over the years, we have partnered with XOXCO and have been inspired by this work,” said Lili Cheng, Corporate Vice President, Conversational AI at Microsoft on Thursday.

Conversational AI is quickly becoming a way in which businesses engage with employees and customers — from creating virtual assistants and redesigning customer interactions to using conversational assistants to help employees communicate and work better together.

According to Gartner, “by 2020, conversational artificial intelligence will be a supported user experience for more than 50 percent of large, consumer-centric enterprises”.

The Microsoft Bot Framework, available as a service in Azure and on GitHub, today supports over 360,000 developers.

Microsoft, PUBG
A sign for Microsoft is seen on a building in Cambridge. VOA

“With this acquisition, we are continuing to realise our approach of democratising AI development, conversation and dialog, and integrating conversational experiences where people communicate,” said Cheng.

Over the last six months, Microsoft has made several strategic acquisitions to accelerate the pace of AI development.

The acquisition of Semantic Machines in May brought a revolutionary new approach to conversational AI.

In July, it acquired Bonsai to help reduce the barriers to AI development by combining machine teaching, reinforcement learning and simulation.

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In September, Microsoft acquired Lobe, a company that has created a simple visual interface empowering anyone to develop and apply deep learning and AI models quickly, without writing code.

“The acquisition of GitHub in October demonstrates our belief in the power of communities to help fuel the next wave of bot development,” Microsoft said. (IANS)

Next Story

With Ovarian Cancer Deaths Set to Spike by 67%, AI to Rescue: Study

However, the scans cannot give clinicians detailed insight into patients’ likely overall outcomes or on the likely effect of a therapeutic intervention

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Cancer
Cancer Ribbon. Pixabay

With the incidence of ovarian cancer likely to increase by 55 per cent in another 15 years or so, researchers have created an artificial intelligence (AI) software to help best treat ovarian cancer that will pave the way for personalised medicine and expedite relief, a new study says.

The mathematical software tool — TEXLab — can also predict what treatment might be most effective for patients with the World Ovarian Cancer Coalition predicting that deaths will likely increase by 67 per cent by 2035 due to this particular cancer.

The technology can be used to identify patients who are unlikely to respond to standard treatments and offer alternatives as ovarian cancer is the sixth most common cancer in women in the UK that usually strikes after menopause or those with a family history of the disease.

Early detection of the disease could improve survival rates, the study noted.

“Long-term survival rate for patients with advanced ovarian cancer is poor despite advancements in treatments. There is an urgent need for new ways,” said lead author Eric Aboagye, Professor at Imperial College London.

For the study, researchers used the software to identify the aggressiveness of tumours in CT scans and tissue samples from 364 women with ovarian cancer.

The patients were then given a score known as Radiomic Prognostic Vector (RPV) which indicates how severe the disease is, ranging from mild to severe.

Cancer patient
Cancer patient.

The findings, published in Nature Communications, showed that the software was up to four times more accurate for predicting deaths from ovarian cancer than standard methods.

In addition, five per cent of patients with high RPV scores had a survival rate of less than two years, results showed.

High RPV was also associated with chemotherapy resistance and poor surgical outcomes, suggesting that RPV can be used as a potential bio-marker to predict how patients would respond to treatments.

“Our technology is able to give clinicians more detailed and accurate information on how the patients are likely to respond to different treatments, which could enable them to make better and more targeted treatment decisions,” said Aboagye.

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Doctors as of now diagnose ovarian cancer in a number of ways, including a blood test followed by a CT scan that uses X-rays and a computer to create detailed pictures of the ovarian tumour.

This helps clinicians know how far the disease has spread and determines the type of treatment patients receive, such as surgery and chemotherapy.

However, the scans cannot give clinicians detailed insight into patients’ likely overall outcomes or on the likely effect of a therapeutic intervention. (IANS)