AI News, BOOK REVIEW: Between the Lines

New Open-Source AI Machine Learning Tools to Fight Cancer

In Basel, Switzerland at this week’s 18th European Conference on Computational Biology (ECCB) and 27th Conference on Intelligent Systems for Molecular Biology (ISMB), IBM will share three novel artificial intelligence (AI) machine learning tools called PaccMann, INtERAcT, and PIMKL, that are designed to assist cancer researchers.

“To the best of our knowledge, there have not been any multi-modal deep learning solutions for anticancer drug sensitivity prediction that combine a molecular structure of compounds, the genetic profile of cells and prior knowledge of protein interactions.” PaccMann’s deep learning solution uses a three-prong data approach, incorporating transcriptomic profiles of cancer cells, protein interactions within cells, and the molecular structure of compounds in order to predict the impact of a drug sensitivity on cancer cells.

The study tested PaccMann’s ability to predict drug sensitivity on over 200,000 drug-cell line pairs in the Genomics of Drug Sensitivity in Cancer (GDSC)—a database that characterizes human cancer cell lines with a wide range of anticancer drugs where sensitivity patterns of cell lines are correlated with genomic data to identify biomarkers that are predictive of sensitivity.

According to a July 22, 2019 IBM Research article written by Matteo Manica and Joris Cadow, “PaccMann not only predicted sensitivity for the drug-cell line pairs more accurately than alternative tools, it also offered explainability, highlighting which specific genes and which portions of the compound’s molecular structure it paid the most attention to while performing the predictions.” There is a wealth of scientific cancer research in scientific publications—harnessing the data manually is extremely time-consuming, and is limited in reach.

 In the Google study, the researchers demonstrated that word vectors can be “successfully applied to automatic extension of facts in Knowledge Bases, and also for verification of correctness of  existing facts,” and that “it is possible to train high-quality word vectors using very simple model architectures, compared to the popular neural network models (both feedforward and recurrent).” In effect, the study showed that it is possible to “compute very accurate high dimensional word vectors from a much larger data set” due to reduced computational complexity.

Making these novel AI deep learning tools open to the public will enable the creation of future models to accelerate scientists in many areas, including drug discovery by pharmaceuticals, life sciences and biotech in the quest to discover better, more targeted treatments with fewer side-effects for cancer patients in the future.

AI In Insurance Use Case #16: Avinew - Disruptor Daily

This interview is part of our new AI in Insurance series, where we interview the world's top thought leaders on the front lines of the intersections between AI and insurance.

After we brought in a new CEO to run Hixme I started thinking about the insurance world and how, with new technology coming to market, there would be a need for new insurance.

DP: This new generation of semi-autonomous and autonomous vehicles are equipped with advanced driver assistance systems (ADAS) and sensors, which will deliver new, real-time driving and usage data.

Avinew has found a way to harness this data, apply the latest AI and machine learning techniques, and develop predictive models of accident risk with a higher degree of precision than previously possible, enabling us to offer insurance at a significant discount to those customers that invest in and utilize the latest ADAS and assisted driving features.

This allows us to underwrite at the speed of tech instead of having to wait for a decade of historical data to populate actuarial tables.

Investors Seek an Edge by Using Technology That Reads Between the Lines

Ever since British economist John Maynard Keynes first declared that investors are prey to people’s urge to act, however irrationally, the financial world has tried to quantify the impact of public sentiment on stock prices.

The technology is still imperfect and still must prove that it can outperform more basic investment strategies like stock index funds.  “If you can systematically and objectively track over time how the facts change, in terms of positive facts and negative facts that emerge in a conference call, say, that could have real value,” says David Wong, an investment analyst with Instinet.

Last month, AlphaSense, a startup that sells its service to hedge funds and financial analysts, introduced technology that sifts though documents to determine the tenor of their language.

It’s not a surprising conclusion considering that the company’s CEO said during an earnings call that his customers—construction companies— had “blood on the floor” due to recessionarytrends.  The company with the most positive sentiment was supply-chain services company Synnex.

In past, with less sophisticated technology, computers assessed language for sentiment by counting the frequency of words in a text, such as “debt,” “layoffs,” “foreclosure.” Modern sentiment analysis can make many more connections between such terms and more distant words in the same paragraph, to understand the context.

However, Mr. Ferragu warns that while the software can speed up work for people in the financial industry, the public nature of the data it bases its opinions on, like conference call transcripts, reduces the benefit.

Over time, rival technologies that conduct the same kind of textual analysis may reach identical conclusions about individual stocks—limiting any trading edge the technology provides.  Then, too, gauging sentiment is tricky because computers have trouble with irony and metaphors.

“These tools can help you source an intuition and check it against the data, but it will never replace your own intuitions about the market.” Correction: An earlier version of this article incorrectly identified the lead investor in AlphaSense's recent funding round.

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In rise of brain implants, blurring lines between man, machine?

It sounds far-fetched: With a computer chip implanted in their brains, humans could boost their intelligence with instant access to the internet, write articles like this one by thinking it rather than typing, and communicate with each other without saying a thing ​– what entrepreneur Elon Musk calls “consensual telepathy.” Of course, it’s not really telepathy.

But it raises important ethical questions, as academic researchers and industry scientists pursue a path that could lead to the merging of human thought with artificial intelligence through the routine use of brain implants.

The entry of companies – and especially the flow of venture capital into the field – raises some important ethical issues. While some wrestle with big philosophical questions like the further blurring of boundaries between man and machine, scientists are focused on the more immediate questions of patient safety and corporate priorities.

That jump in electrodes should allow its system to capture far more neuron activity.  The company also showed off a robot that can connect the electrodes to the brain more accurately than a human can.

“We have an obligation to a slow science.” “The thing that worries me is if they make a bad mistake,” says John Donoghue, a widely recognized neuroscientist, now at Brown University, who founded an early startup to work on computer-brain interfaces.

But some visionaries, like Mr. Musk, dream of a much larger market sometime in the future where ordinary people might opt for a brain implant to boost their intelligence in the way some now have their eyes lasered to improve their eyesight.

But “with a high-bandwith brain-machine interface, I think we can actually go along for the ride and we can effectively have the option of merging with A.I.”  “It’s different worlds,” says Helen Mayberg, a neurologist at Mount Sinai in New York who pioneered the use of deep-brain stimulation for treatment-resistant depression.

… I think we are really a long way off before you get a good enough interface that it’s going to give you a significant advantage.” Other technologies, such as plastic surgery, have moved from strictly helping accident victims to enhancing body features for anyone.

Building the AI-Powered Organization

We’ve surveyed thousands of executives about how their companies use and organize for AI and advanced analytics, and our data shows that only 8% of firms engage in core practices that support widespread adoption.

Firms struggle to move from the pilots to companywide programs—and from a focus on discrete business problems, such as improved customer segmentation, to big business challenges, like optimizing the entire customer journey.

While cutting-edge technology and talent are certainly needed, it’s equally important to align a company’s culture, structure, and ways of working to support broad AI adoption.

Having business and operational people work side by side with analytics experts will ensure that initiatives address broad organizational priorities, not just isolated business issues.

Diverse teams can also think through the operational changes new applications may require—they’re likelier to recognize, say, that the introduction of an algorithm that predicts maintenance needs should be accompanied by an overhaul of maintenance workflows.

The new system rapidly analyzed the vast range of scheduling permutations, using first one algorithm to distill hundreds of millions of options into millions of scenarios, and then another algorithm to boil down those millions into just hundreds, ranking the optimal schedules for each participant.

(Our research shows that the majority of workers will need to adapt to using AI rather than be replaced by AI.) When a large retail conglomerate wanted to get its employees behind its AI strategy, management presented it as an existential imperative.

In sharing their vision, the company’s leaders put a spotlight on workers who had piloted a new AI tool that helped them optimize stores’ product assortments and increase revenue.

For example, if a company has relationship managers who pride themselves on being attuned to customer needs, they may reject the notion that a machine could have better ideas about what customers want and ignore an AI tool’s tailored product recommendations.

The bank created a booklet for relationship managers that showed how combining their expertise and skills with AI’s tailored product recommendations could improve customers’ experiences and increase revenue and profit.

In one of our surveys nearly 90% of the companies that had engaged in successful scaling practices had spent more than half of their analytics budgets on activities that drove adoption, such as workflow redesign, communication, and training.

Automated processes that don’t need human intervention, such as AI-assisted fraud detection, can deliver a return in months, while projects that require human involvement, such as AI-supported customer service, are likely to pay off over a longer period.

An Asian Pacific retailer determined that an AI initiative to optimize floor space and inventory placement wouldn’t yield its complete value unless the company refurbished all its stores, reallocating the space for each category of goods.

The tool provided only a small fraction of the total return anticipated, but the managers could get the new items into stores immediately, demonstrating the project’s benefits and building enthusiasm for the multiyear journey ahead.

Often leaders simply ask, “What organizational model works best?” and then, after hearing what succeeded at other companies, do one of three things: consolidate the majority of AI and analytics capabilities within a central “hub”;

One consolidated its AI and analytics teams in a central hub, with all analytics staff reporting to the chief data and analytics officer and being deployed to business units as needed.

Our research shows that companies that have implemented AI on a large scale are three times as likely as their peers to have a hub and 2.5 times as likely to have a clear methodology for creating models, interpreting insights, and deploying new AI capabilities.

We’ve seen many organizations squander significant time and money—spending hundreds of millions of dollars—up front on companywide data-cleaning and data-integration projects, only to abort those efforts midway, realizing little or no benefits.

In contrast, when a European bank found that conflicting data-management strategies were hindering its development of new AI tools, it took a slower approach, making a plan to unify its data architecture and management over the next four years as it built various business cases for its AI transformation.

To encourage customers to embrace the AI-enabled services offered with its smart, connected equipment, one manufacturer’s sales and service organization created a “SWAT team” that supported customers using the product and developed a pricing plan to boost adoption.

By concentrating its data scientists, engineers, and many other gray-area experts within the hub, the company ensured that all business units and functions could rapidly access essential know-how when needed.

For example, an organization might have high business complexity and need very rapid innovation (suggesting it should shift more responsibilities to the hub) but also have very mature AI capabilities (suggesting it should move them to the spokes).

Each generally includes the manager in charge of the new AI tool’s success (the “product owner”), translators, data architects, engineers and scientists, designers, visualization specialists, and business analysts.

For example, at the Asian Pacific retailer that was using AI to optimize store space and inventory placement, an interdisciplinary execution team helped break down walls between merchandisers (who determined how items would be displayed in stores) and buyers (who chose the range of products).

By inviting both groups to collaborate on the further development of the AI tool, the team created a more effective model that provided a range of weighted options to the buyers, who could then choose the best ones with input from the merchandisers.

To this end some are launching internal AI academies, which typically incorporate classroom work (online or in person), workshops, on-the-job training, and even site visits to experienced industry peers.

Here the focus is on constantly sharpening the hard and soft skills of data scientists, engineers, architects, and other employees who are responsible for data analytics, data governance, and building the AI solutions.

Strategic decision makers, such as marketers and finance staff, may require higher-level training sessions that incorporate real business scenarios in which new tools improve decisions about, say, product launches.

They regularly meet with staff to discuss the data, asking questions such as “How often are we right?” and “What data do we have to support today’s decision?” The CEO of one specialty retailer we know is a good example.

One airline company, for instance, used a shared scorecard to measure rate of adoption, speed to full capability, and business outcomes for an AI solution that optimized pricing and booking.

The CEO of the specialty retailer starts meetings by shining a spotlight on an employee (such as a product manager, a data scientist, or a frontline worker) who has helped make the company’s AI program a success.

For instance, he promoted the category manager who helped test the optimization solution during its pilot to lead its rollout across stores—visibly demonstrating the career impact that embracing AI could have.

Since their sales incentives were also closely tied to contracts and couldn’t easily be changed, the organization ultimately updated the AI model to recognize the trade-off between profits and the incentives, which helped drive user adoption and lifted the bottom line.

As they work more closely with colleagues in other functions and geographies, employees begin to think bigger—they move from trying to solve discrete problems to completely reimagining business and operating models.

Companies that excel at implementing AI throughout the organization will find themselves at a great advantage in a world where humans and machines working together outperform either humans or machines working on their own.

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