AI News, MIT artificial intelligence

MIT, Takeda Partner to Develop Healthcare Artificial Intelligence

January 07, 2020 -MIT School of Engineering and Takeda Pharmaceuticals Company Limited have partnered to use artificial intelligence tools to benefit health and drug development.

The MIT-Takeda Program will fund 6-10 flagship research projects per year in machine learning and healthcare, focusing on areas such as disease diagnosis, prediction of treatment response, development of novel biomarkers, process control and improvement, drug discovery, and clinical trial optimization.

The program will also provide eleven annual fellowships that will support graduate students working at the intersection of AI and health, creating significant programming for young students.

In December 2017, the organization announced a new project that aims to align innovative pharmaceutical development with real-world patient care.

The initiative seeks to break down siloes between pharma and patient care by equipping clinical trials with a real-world evidence “learning engine.”

“Our goal is to integrate a number of emerging but fragmented policy, process, and technology innovations into a system that works better for everyone, and especially for patients.”

5 steps to ‘people-centered’ artificial intelligence

As companies double down on business initiatives built around technologies like predictive analytics, machine learning, and cognitive computing, there’s one element they ignore at their peril — humans.

That was the message from a pair of experts at a recent MIT Sloan Management Review webinar, “People-Centered Principles for AI Implementation.” As organizations push forward on their artificial intelligence  journey, they should strive to put individuals at the center of the design process, the experts advised.

Fueled by the rise of cloud computing, there is now ample memory, storage, and computational horsepower to handle sophisticated algorithms that were developed in the past but not put to use due to the limitations of technology, said Bray, who is also a senior fellow at the Florida Institute for Human &

“That’s the data-decision paradigm we’re aiming for.” While AI has cycled through periods of heated interest and winters of stagnation, the time has come for all to get serious about advancing people-centered AI initiatives to stay abreast of the competition, said Bray and his co-presenter, R.

Ensure employees and external stakeholders understand how any AI system arrives at its contextual decisions —specifically, what method was used to tune the algorithms and how decision-makers will leverage any conclusions.

Organizations must also be able to reverse what deep learning knows: The ability to unlearn certain knowledge or data helps protect against unwanted biases in data sets.

And data should be regularly assessed — for example, whether previously approved trusted data is still relevant or unreliable, or if queued data has a newfound role in improving the existing pool of trusted data for specific actions.

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The Invention of “Ethical AI”

In the penal case, our research led us to strongly oppose the adoption of risk assessment tools, and to reject the proposed technical adjustments that would supposedly render them “unbiased” or “fair.” But the Partnership’s draft statement seemed, as a colleague put it in an internal email to Ito and others, to “validate the use of RA [risk assessment] by emphasizing the issue as a technical one that can therefore be solved with better data sets, etc.” A second colleague agreed that the “PAI statement is weak and risks doing exactly what we’ve been warning against re: the risk of legitimation via these industry led regulatory efforts.” A third colleague wrote, “So far as the criminal justice work is concerned, what PAI is doing in this realm is quite alarming and also in my opinion seriously misguided.

and post-deployment evaluation.” To be sure, the Partnership staff did respond to criticism of the draft by noting in the final version of the statement that “within PAI’s membership and the wider AI community, many experts further suggest that individuals can never justly be detained on the basis of their risk assessment score alone, without an individualized hearing.” This meek concession — admitting that it might not be time to start imprisoning people based strictly on software, without input from a judge or any other “individualized” judicial process — was easier to make because none of the major firms in the Partnership sell risk assessment tools for pretrial decision-making;

I argued, “If academic and nonprofit organizations want to make a difference, the only viable strategy is to quit PAI, make a public statement, and form a counter alliance.” Then a colleague proposed, “there are many other organizations which are doing much more substantial and transformative work in this area of predictive analytics in criminal justice — what would it look like to take the money we currently allocate in supporting PAI in order to support their work?” We believed Ito had enough autonomy to do so because the MIT-Harvard fund was supported largely by the Knight Foundation, even though most of the money came from tech investors Pierre Omidyar, founder of eBay, via the Omidyar Network, and Reid Hoffman, co-founder of LinkedIn and Microsoft board member.

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