What is the law but a series of algorithms? Codified instructions proscribing dos and don’ts—ifs and thens. Sounds a lot like computer programming, right? The legal system, on the other hand, is not as straightforward as coding. Just consider the complicated state of justice today, whether it be problems stemming from backlogged courts, overburdened public defenders, and swathes of defendants disproportionately accused of crimes.

So, can artificial intelligence help?

Very much so. Law firms are already using AI to more efficiently perform due diligence, conduct research and bill hours. But some expect the impact of AI to be much more transformational. It’s predicted AI will eliminate most paralegal and legal research positions within the next decade. Could judges and lawyers share the same fate? My coauthor Michael Ashley and I spoke to experts about AI’s impact on the legal system for our upcoming book, Own the A.I. Revolution: Unlock Your Artificial Intelligence Strategy to Disrupt Your Competition.

“It may even be considered legal malpractice not to use AI one day,” says Tom Girardi, renowned civil litigator and the real-life inspiration for the lawyer in the movie, Erin Brockovich. “It would be analogous to a lawyer in the late twentieth century still doing everything by hand when this person could use a computer.”


There are many reasons to believe AI could benefit the legal industry in ways as meaningful as the personal computer. Currently, the legal system relies on armies of paralegals and researchers to discover, index, and process information. For law firms at present, this reliance can be expensive, driving up the rates they charge. And in understaffed public defender offices, investigators can only spend a few minutes interviewing each of their clients, greatly diminishing the service they can provide.

However, for just a fraction of the time and expense, AI could be used to conduct time-consuming research, reducing the burdens on courts and legal services and accelerating the judicial process. There are also situations where using AI might be preferable to interacting with a human, such as for client interviews. For instance, it’s been demonstrated people are more likely to be honest with a machine than with a person, since a machine isn’t capable of judgment.


Of course, AI can’t replace all means of collecting information. There are instances in which depositions would be more conducive to fact-gathering. Still, when preparing for a cross-examination of an expert witness, AI could be effectively deployed to determine every case in which a particular witness testified, what his/her opinions were, and how juries reacted, much faster and more thoroughly than any human investigator ever could.

Such effectiveness is great, but will lawyers panic when they can’t bill as many hours? Not according to Girardi. He believes those firms willing to adopt AI will possess a strategic advantage. “It’s a lawyer’s job to solve a problem as quickly and inexpensively as possible,” Girardi explains. “AI will be a godsend because it’ll give lawyers the information they need to resolve conflicts faster.”

Yes, AI-wielding lawyers wouldn’t be able to technically bill as many hours since the AI would work much faster than they ever could; however, according to Girardi, these attorneys’ enhanced effectiveness would likely garner repeat business and lead to more clients. “If a lawyer can use AI to win a case and do it for less than someone without AI, who do you think the client will choose to work with next time?” says Girardi. Accordingly, the promise for law firms using AI is that they will still be able to generate the same amount or even more revenue while expanding their client rosters. Conversely, firms too slow to adapt to AI and automation will suffer a competitive disadvantage.

While conventional wisdom still suggests job security for lawyers and judges is more secure than other professions, there have been calls to relieve our backlogged court system by outsourcing minor cases to AI. To this end, some courts are even considering using AI to determine eligibility for bail by detecting behavioral patterns indicating flight risk — a decision flesh-and-blood judges traditionally made in the past.

Courtrooms are likely to transform in other meaningful ways due to technological advances. Girardi believes courts might one day welcome AI technology to aid with jury selection. “If a civil dispute concerns a matter of fact—Did the doctor cut off the wrong leg?—today, it’s largely settled out of court,” says Girardi. “However, if a case concerns the interpretation of the law—Did he or she wait too long before performing a procedure? Did a doctor make a bad judgment call?—the case can go to trial, which is why the philosophical makeup of a jury is so important.”

AI could be valuable in a trial setting because it could predict such philosophical makeups. Adept at rapidly collecting important information, it could gather data about potential jurors, including their accident history, if they have served before, the verdicts of those trials, and a juror’s political and charitable affiliations. AI could also be used to analyze facial reactions and body language indicating how a potential juror feels about an issue. Before a potential juror even answers a question, the movement of his or her eyes, a change in skin coloration, or a shift in body positioning could nonverbally communicate an emotional response demonstrating a positive or negative bias. Such data could be used for optimal jury selection, facilitating greater fairness.

In spite of such developments inside the courtroom, it’s nonetheless hard to imagine how trial lawyers might be replaced by artificial intelligence. For now, a human’s unique ability to create empathy with jurors and judges alike makes them indispensable to legal deliberations. But what if judges were one day replaced by robots? After all, we know humans are fallible creatures, prone to prejudices and biases.

Song Richardson, Dean of the University of California-Irvine School of Law, worries about just this possibility. “Why does someone become a lawyer or judge? It’s certainly not to become a cog in the wheel of an assembly line system of justice. The fact that we have backlogs resulting in a failure to give people the individualized attention they deserve tells us there’s something fundamentally wrong with our justice system. Expediting the mass processing of people using AI isn’t the answer. It’s the opposite of justice.”

Richardson believes AI can benefit the legal profession, yet she cautions us to be careful how we implement it. Even the best AI needs to be taught, which means it can only be as objective as the people who teach it. “People often view AI and algorithms as being objective without considering the origins of the data being used in the machine-learning process,” says Richardson, who specializes in the dangers of unconscious bias. “Biased data is going to lead to biased AI. When training people for the legal profession, we need to help future lawyers and judges understand how AI works and its implications in our field.”

In spite of these concerns, Richardson still believes the net impact of new technology will be positive. Lawyers and judges are only as good as the information they receive, and AI has the potential to significantly increase the quality of information. Still, no matter how sophisticated the technology becomes, she and Girardi both agree it will never be a substitute for the judgment and decision-making only humans can provide. “AI isn’t going to replace the need for critical thinking. We still need to prepare students to think like lawyers, and I don’t think that’s ever going to change,” says Richardson.

Though no consensus exists yet as to how AI will ultimately shape the legal profession, we do know AI is poised to transform nearly every facet of our lives, and the new technologies it’s powering will create a host of unprecedented legal issues, including ownership, liability, privacy and policing. For a taste of what’s coming, just consider this: when self-driving cars start getting into accidents, who will be deemed responsible? The car owner? The manufacturer? The software designer? The very fact these are complicated issues soon to be exacerbated by unprecedented technology reveals the need for more lawyers, but not just any kind of lawyers. We need those capable of making sense of our rapidly evolving society.

“What worries me is that we won’t have lawyers who understand algorithms and AI well enough to even know what questions to ask, nor judges who feel comfortable enough with these new technologies to rule on cases involving them,” says Richardson. In light of such valid concerns, it is becoming increasingly clear our law schools must prepare tomorrow’s lawyers to use the new technology. But even this isn’t enough. We also need today’s practicing counsel and judges to grasp AI and all it promises to better serve and protect our fellow humans.

Read Source Article: Forbes Neil Sahota (萨冠军), is an IBM Master Inventor, United Nations (UN) Artificial Intelligence (AI) subject matter expert, and Professor at UC Irvine, with 20+ years of business experience.

In Collaboration with HuntertechGlobal

Christie’s held the first-ever auction of art created by artificial intelligence.

 Four months ago, Christie’s said it held the first-ever auction of art created by artificial intelligence. The $432,500 sale sparked a controversy among critics over whether it’s really AI-generated if a human was involved in making the portrait. 

Next month, a new Sotheby’s sale in London may end the dispute and could even presage a boom in AI-generated art, which until now has been relatively scarce. 
The firm will take bids for a piece made by German computer scientist Mario Klingemann on March 6 in London. This one was fully curated by a computer, according to the auction house. That’s different from the AI portrait the sold in October, which included some human intervention by Paris-based art collective Obvious. 

Entitled Memories of Passersby I, Klingemann’s auction debut is made up of an AI “brain” that beams an endless stream of images of distorted faces onto two screens, the result of algorithms. Blurry faces come into focus, though none of the people portrayed ever existed. 

Still, purists could insist the work isn’t fully computer-generated, as Klingemann admits he had to build the machine. But now it’s ready to create art endlessly, and soon it will be up to bidders to decide. After the Obvious sale smashed the original estimate of $10,000, Christie’s has put the estimate for this sale at 30,000 ($39,000) to 40,000 pounds. 

Read Source Article The Economic Times

In Collaboration with HuntertechGlobal


Artificial Intelligence (AI) and Data Visualization can seem like an unlikely marriage. AI techniques often work as a black box: we cannot know how the AI has reached its conclusion. This can raise uncomfortable questions: think of a medical diagnosis, or the screening of job applicants: if we cannot see inside the black box, we can’t know whether the AI made a serious mistake, or reflected our implicit bias. When the AI becomes a veil between us and the data it makes us uncomfortable and it takes our own intuition and insight out of the game.

But AI can help us see the data, becoming a crucial help to our own analysis and judgement.

Leo Meyerovich, CEO of Graphistry, discusses in this podcast interview a number of areas where AI-driven data visualization greatly facilitates the work of human analysts: from fraud prevention to health care, supply chain management, customer analysis, all the way to fighting human trafficking and spotting election-influencing tactics.

Across all these applications, says Leo Meyerovich, “The dream is some sort of a black box […but] I found zero systems that are fully automated.” There is always a human in the loop. Sometimes it’s to spot where the AI stumbles on a ‘false positive’, like a legitimate transaction flagged as fraudulent. Sometimes the analyst’s experience is invaluable to accelerate the process: the human knows what to look for and has a better sense of the context than the AI.


In all these fields, AI provides invaluable help.  Human intuition, experience and decision-making continue to play the central role across economic activities. But now we have realized that data can help us make better decisions, and we have learnt to harvest and store prodigious quantities of data—so large that we struggle to make sense of them on our own.

Especially with the rise of complex global supply chains, most companies today are exposed to a very large number of evolving factors, from commodity prices to transportation costs to economic developments across the world (think of the recent impact of China's growth slowdown). How can you capture the interrelated impact of all these factors on your business?

A picture is worth a thousand words, but faced with a flood of data, humans need help building the right picture. This will most likely not be as simple as a bar chart--it might be a diamond, an evolving set of color-coded clouds and cluster, or filaments and waves showing the spread of malware . Look at the demos of Graphistry or other AI/Data Visualization companies, and you will see how AI can make data representation more creative, elegant and, most importantly, intuitive. AI can now take gigantic datasets and show us patters and correlations that help our own intelligence do its best job. Just as we have to do the hard work of organizing and labeling the data in a way that the AI can usefully process, so the AI can then devise the best designed input for our own processing power. It’s a great example of human-machine partnerships.

In this context, AI can also help capture and transfer learning within organizations: once an experienced analyst, together with the AI, has devised an effective set of steps and visualizations to analyze a problem, the AI can remember that and build it into a set of tools ready for a junior analyst coming on board. This could prove especially helpful to organizations that experience very high staff turnover—and to industries that face a workforce aging problem, with large experienced cohorts retiring and a smaller cohort in the pipeline.

The more complex the business, the greater the number of moving parts and variables, the greater value we can get from an AI that helps us see through the fog of data.

Marco Annunziata is Co-Founder of Annunziata + Desai Advisors and former Chief Economist and Head of Business Innovation Strategy at GE. Follow him on Twitter

Read Source Article: Forbes
In Collaboration with HuntertechGlobal

During a drug’s journey from bench to bedside, there are multiple touchpoints where machine learning can provide value. It can be difficult to decipher which AI tools in development stand to offer real advances and where AI’s benefits have been exaggerated.

For decades, artificial intelligence has held science fiction-like promise. Today, nearly every field has been impacted by AI and many have been transformed. Marketing, finance, the legal sector and education, among other industries, have seen how machine learning can digest impossibly large sets of data and provide insights into improvements and efficiencies. But one of AI’s biggest promises lies in the healthcare field. So how can AI improve healthcare, starting from early stage drug discovery all the way to patient care? And how can the industry steer development and address challenges to ensure that AI tools improve over time and build stakeholder buy-in?

When marketing drives undue hype

During a drug’s journey from bench to bedside, there are multiple touchpoints where machine learning can provide value. It can be difficult to decipher, however, how many of the AI tools in development stand to offer real advances and where AI’s benefits have been exaggerated. In many cases, it is too soon to tell. This hasn’t stopped some companies from marketing this technology as a game-changer destined to transform the industry and cure disease, inevitably creating a tension between the experts and customers.

Leslie Wheeler of Spectrum Science

Leslie Wheeler, an Executive Vice President at Spectrum Science Communications, observed that entrepreneurs tackling healthcare challenges must be mindful that this industry is heavily regulated.

“Many of the players in AI in healthcare come from a tech background and may not necessarily understand the nuances in healthcare,” she said. “The regulatory landscape is complex and the healthcare audience, by nature, is skeptical. Lofty claims might fly in the tech space, but not in medicine. Science doesn’t deal in vaporware.”  

Eric Moorhead, Senior Scientific Executive with Spectrum, agrees.

“One of the core tenets of the tech industry is disruption. I’m not convinced that healthcare as a whole can — or should — be disrupted. While I think there are efficiencies to be found, medicines are developed and regulated at a deliberate pace that prioritizes patient benefit and safety. The idea that steps can be skipped gives me pause.”

Where does AI offer hope in healthcare?

Expectation management will be crucial for the companies pursuing AI solutions at the intersection of technology and life sciences. In 2017, venture capitalists poured nearly $1.3 billion into these businesses across 103 deals, according to data from KPMG.

AI-based approaches that have been clinically validated have revolved around digital pathology, molecular diagnostics and medical imaging for clinical decision support. Diagnostics company Genomic Health’s molecular diagnostic Oncotype Dx, for example, has been used to predict the likelihood of recurrence for early stage breast cancer but has also been used for determining chemotherapy treatment options.

The intersection of technology and drug development is an area that has generated a great deal of excitement. The cost of drug development is so high, and applications which analyze data to identify suitable drug targets, expand indications for drugs already approved and speed up clinical trial recruiting offer the potential to reduce drug development costs. The goal is also to identify potential problems earlier to avoid costly late stage failures.

It is important to acknowledge that one underlying source of tension is the fear that AI technology will lead to significant job cuts if AI tools live up to the promise of automating time-consuming tasks.

Eric Moorhead of Spectrum Science

“My specific background is medicinal chemistry,” said Moorhead. “While I don’t think machines are coming for chemistry jobs, I do think that AI will be a valuable tool to help researchers narrow targets, generate leads and, hopefully, advance candidates quicker into the clinic.”

Many of the big names in the industry are beginning to experiment with AI in drug development, favoring partnership models with smaller AI companies. Sanofi and GSK each partnered with a Scottish AI company, Exscientia, with millions in milestone payments on the line, to streamline drug discovery. Sanofi’s collaboration seeks to identify synergistic drug targets for metabolic diseases, and GSK’s collaboration seeks to discover novel and selective small molecules across multiple therapeutic areas.

Additionally, AstraZeneca and Sanofi have each partnered with BERG, a tech business that applies machine learning analytics to patient biology. AstraZeneca’s partnership seeks to utilize AI to identify biological targets and treatments for neurological diseases including Parkinson’s disease. Sanofi’s partnership is looking for potential biomarkers of seasonal influenza vaccinations to better predict response.

Other partnerships focusing on early-stage discovery technologies include Insitro, started by Coursera co-founder Daphne Koller. Attracting investments from a high-powered group of venture capital firms including Andreessen Horowitz, GV (formerly Google Ventures), Third Rock, ARCH Venture Partners and Foresite Capital, Insitro plans to train machine learning models to help “address key problems in the drug discovery and development process,” according to a blog post by Koller. The company’s ambitious goal is to create a way to develop drugs that are cheaper, faster and have a higher success rate than traditional models. Doing her part to minimize over-promising claims, Koller noted in the post that she does not expect this approach to offer a “magic bullet” so much as add another option for drug developers.

AI has shown some preliminary successes on the other end of the drug pipeline. Machine learning can help better match patients with clinical trials, cutting down the time it takes to appropriately conduct and run a clinical study. One of the companies in this area is Deep 6 AI. It uses AI and natural language processing to generate clinical data points – symptoms, diagnoses, treatments, genomics, lifestyle data — to match complex clinical trial criteria. One assessment of the company’s technology found and validated 58 eligible matches for a non-small cell lung cancer trial within minutes compared with traditional recruitment methods which took six months. In addition, AI can help determine anticipated responses in said trials. In September, GNS Healthcare announced it had joined forces with Amgen and the Alliance for Clinical Trials in Oncology to identify factors that drive treatment response in patients with metastatic colorectal cancer.

The rise of industry alliances

To foster the creation and validation of AI software for drug development, a few alliances have come into being between academia and pharma companies, as well as big pharma and technology startups. Last year, MIT formed a group to transform the process of drug design and manufacturing through the design of software for the automation of small molecule discovery and synthesis. Called Machine Learning for Pharmaceutical Discovery and Synthesis Consortium, the group includes industry players such as Amgen, AstraZeneca, BASF, Bayer, GSK, Lilly, Janssen, Merck, Novartis, Pfizer, Sunovion, and WuXi.

The Alliance for Artificial Intelligence in Healthcare will mark its official launch at the JP Morgan Healthcare Conference this week. Among its goals are:

  • Establish standards for the industry definition of AI and how it can be applied to existing industries to reduce risk and improve outcomes
  • Address the hype machine by setting clear and pragmatic expectations for AI and monitoring the news to track how and when the technology’s outcomes are exaggerated
  • Lead the effort to form industry partnerships with regulatory bodies to help hammer out regulations needed for the use and implementation of AI in biomedical discovery and patient care.

Other goals include facilitating data sharing and open access to key findings and setting up a model and testing approach for quality control. These developments make sense to Wheeler, who sees these groups as essential for AI in healthcare to establish standard practices.

“To facilitate the discussion and to accelerate the adoption of these approaches in clinical trials and decision making, more stakeholders will need to discuss and debate,” Wheeler said.  “I am very excited to see that new groups have formed like the Alliance for Artificial Intelligence that will be looking at how to standardize AI and the policies and regulations surrounding it.”


2019 has the potential to be an incredibly critical year for the advancement of AI in healthcare. The formation of the Alliance for Artificial Intelligence in Healthcare is a positive development and recognition that establishing standards and best practices will be crucial for companies to hone their AI tools.

Former Flatiron Health Chief Medical Officer Amy Abernathy is poised to become principal deputy commissioner with the FDA next month. In her role at the oncology data company, which Roche acquired last year, she addressed the challenge of upgrading the unstructured data from electronic health records so that it could be useful for clinicians and researchers. That is the kind of insight and industry knowledge that will be crucial for the FDA to work well with companies developing AI tools in healthcare.

Moorhead said this year will be pivotal in gauging the progress and potential of AI in healthcare. In 2019, he is looking to see where AI technologies are beginning to show a return on investment.

“I would like to see more in the way of case studies that demonstrate how companies’ AI technologies are providing value. There’s a lot of excitement around the incorporation of AI in early stage drug development,” he said. “I’m interested in seeing how it all comes together — I think skeptics are waiting for when these ideas begin to provide tangible benefits. I’m sure investors are as well.”

Going forward, transparency and rigorous validation will be crucial to maintaining positive momentum of AI in healthcare. Additionally, it is important to recognize that while AI offers a valuable support mechanism, it is not itself a panacea for healthcare. There will inevitably be setbacks as with any innovative technology. A frank assessment of these failures and insights on how to improve will keep this burgeoning area honest, reinforce stakeholder support and ensure the technology’s future long term.

Read Source Article:MedCityNews

In Collaboration with HuntertechGlobal

#AI #MachineLearning #DeepLearning #Research #ArtificialIntelligence #Analytics #DataScience #Technology #Marketing #BigData #AIHealthcare


Northwell Health, a New York-based healthcare network, is integrating predictive AI software into the electronic medical records (EMRs) at 15 of its hospitals to identify patients at risk of being readmitted to the hospital.

The healthcare organization’s integration plan is a part of an overall effort to better identify at-risk patients and factors, including social determinants of health, that may contribute to a patient’s increased risk. The hope is that the AI software from Jvion, a Georgia-based AI healthcare company, will “seamlessly” integrate with clinical workflows, allowing caregivers access to its risk and recommendation outputs, according to a press release.

The software will help manage patients by flagging those at risk, while also identifying clinical drivers that contribute to their likelihood of readmission or avoidable admission. The software will also provide individualized recommendations to best mitigate those risks.

“Advanced technology is a critical part of our commitment to creating a health system that is the highest value in the country,” Kristofer Smith, MD, senior vice president of population health management at Northwell Health Solutions, said in a statement. “By incorporating the proven AI delivered by Jvion’s Cognitive Machine, we will extend our ability to deliver quality, safe and cost-effective care.”

“Northwell Health is taking the lead in preventing avoidable events and ensuring that care is delivered in the most effective way,” John Showalter, MD, chief product officer for Jvion, said in a statement. “Preventing a deterioration is complicated and dynamic. Jvion’s Cognitive Machine has made significant impacts to various readmission and avoidable admission programs across the country and we are very proud to bring it to Northwell Health.”

Read Source Article: AI in Healthcare

In Collaboration with HuntertechGlobal

#AI #MachineLearning #DeepLearning #Research #ArtificialIntelligence #Analytics #DataScience #Technology #Marketing #BigData #AIHealthcare

© copyright 2017 www.aimlmarketplace.com. All Rights Reserved.

A Product of HunterTech Ventures