Everyone questions an emerging technology based on ethics, regulations and implications. Artificial intelligence faces this test as adoption continues in consumer and enterprise spaces. Enterprises are not doing well in deployment of AI and struggle to align this technology with their strategic goals.

The COVID-19 pandemic continues with researchers and AI experts working on a vaccine solution. AI real time detection of COVID-19 is combating the virus by detecting those with fever symptoms and issuing alerts on proximity of suspect cases.

The BeAware application is the latest technology using artificial intelligence to search those suspected to contract the virus within a given location. Governments around the world are using contact tracing apps to monitor movements and confirming suspect cases as a measure to limit spread of COVID-19.

These and more insights on our Weekly AI Update

Do you trust AI?

Artificial intelligence is one of the most transformational technologies of our age. It is necessary to assess security risks and pertinent questions before the technology starts to conquer the world. People become concerned about security issues when a new innovation emerges, and — sometimes too late. We will likely see a demand for AI security in the 2020s.

We should care about this possible future today, as numerous critical incidents have taken place, such as the Tesla car incident. International efforts are growing to develop regulations and ethical guidelines for AI.

G20 leaders signed a statement endorsing a few basic ethical principles¹ for AI on June 29, 2019. As one of the first countries the US discussed the potential problems of AI security, such as adversarial attacks, in its AI strategy “The National Artificial Intelligence Research And Development Strategic Plan: 2016.”

One of the strategies was to ensure the safety and security of AI systems at every stage in the document. Many leading countries published similar documents later in which security was among the considered issues. The UK government interim strategy considered AI to be a key technology trend and a necessary tool in identifying and responding to security threats.

AI Support during COVID-19 Pandemic

Bahrain, Saudi Arabia and the United Arab Emirates in the Middle East are using artificial intelligence tools to halt the spread of the coronavirus pandemic.

They are using speed cameras, drones, and robots to ensure the movement is limited and social distancing is in place.

Governments can monitor those who have tested positive for coronavirus, and limit their exposure via location-based contact tracing. The ability of AI to crunch large amounts of data² has allowed governments worldwide to collect information and stop the pandemic.

An application called ‘BeAware’ allows residents to track proximity to someone with COVID19. It uses location data to alert individuals in the event they approach an active case.

The State of Enterprise AI

NLP and #deeplearning are capable of providing considerable value to organizations that implement them. But “7 out of 10 companies surveyed report minimal or no impact from AI so far. Among the 90% of companies that have made some investment in AI, fewer than 2 out of 5 report business gains from AI in the past three years.

This number improves to 3 out of 5 when we include companies that have made significant investments in AI. This means 40% of organizations making significant investments in AI do not report business gains from AI

Firms report ongoing interest and an active embrace of AI technologies and solutions, with 91.5% of firms reporting ongoing investment in AI. Only 14.6% of firms report that they have deployed AI capabilities³ into widespread production.

The percentage of respondents agreeing that their pace of investment in AI and big data was accelerating fell from 92% in 2018 to 52% in 2019. The top 3 challenges with AI were implementation issues, integrating AI into the company’s roles and functions, and data issues — all factors involved in large-scale deployment.

Data Driven Business Transformation

The growing skills gap is becoming a great motivator for increased automation, driven by artificial intelligence and #machineLearning. There will be business winners and losers after this pandemic.

Technologies will work as a critical differentiator to create a wide range of business advantages for themselves both during and after the pandemic.

Executives, especially CIOs will be able to view and act on better information and more in-depth analytics, enabling them to drive a faster business transformation with a combination of AI and ML.

The technological development has driven expectations more towards predictive business intelligence than ever before. The task of automation becomes much simpler if you have highly accurate predictive information.

Computing systems are capable to deliver an entire world of historical data and analytical information with just a few clicks now. Intelligence and forecasting data from computer systems are the true crown jewels.

AI and ML technologies are shining today as tools to help pull insights from the massive COVID-19 data pools. AI will hopefully help bring the spread of this pandemic to its heels with fast access and manipulation of data. The combination of AI and ML have the capability to streamline any repetitive task⁴.

Skipping Proof of Concept and Scaling

C-suite executives know they need to integrate AI capabilities to stay competitive but get stuck focusing on the wrong details or building a model to prove a point rather than solve a problem. According to Harvard Business Review, 3 out of 4 executives believe that they risk going out of business entirely if they don’t scale AI in the next 5 years.

Harvard Business Review offers a radical solution: Kill the proof of concept. Go right to scale.

They came to this solution after surveying 1,500 C-suite executives across 16 industries in 12 countries. They discovered that while 84% know they need to scale AI across their businesses to achieve their strategic growth objectives, only 16% of them have actually moved beyond experimenting with AI⁵.

The companies that attempted scaling twice as often, succeeding at their scaling initiatives twice as often because they were structured correctly completing scaling projects more quickly spending less money on pilots & fully scaled deployments.

The largest banking group in the Nordics, Nordea needed a chatbot to help with customer service. Nordea had a structure for testing and development in place — so they skipped the proof of concept and went right to scale.

So commit to action and make sure the right team is in place.

Can AI help to combat against COVID-19?

Scientists and researchers are working heartily to find treatments and develop a vaccine for coronavirus. Artificial intelligence technologies are emerging as key solutions to combating coronavirus where Israel is well-positioned as a well-known country for its strength in deep tech and home to a vibrant AI ecosystem that has been growing rapidly over the past few years.

The unique tech ecosystem of Israel includes companies and startups that utilize AI technologies in healthcare, #cybersecurity, autonomous driving, and many other fields in the country.

Israeli health care system slowed-down the spread of the COVID19 with some of the life-saving technologies. An AI-based triage platform in Israel gives public health officials continuous monitoring of the patterns in which the virus spreads.

Diagnostic Robotics¹⁰ which is originally developed by an Israeli company adapted to tackle the current pandemic, offers an analytics tool that produces risk assessment and predictive models⁶ allowing a faster and better targeted medical response.

Diagnostic Robotics has raised $24 million so far from investors and joined the global fight against COVID-19 by giving countries access to their technology at a cost.

Clinical Trials for COVID-19

British pharmacologist Peter Richardson explained that he had identified a drug that might help people infected with a new virus spreading in China. Benevolent AI, a London startup where Peter Richardson is vice president of pharmacology, developed an artificial intelligence software that might help.

A kind of search engine on steroids that combines drug industry data with nuggets gleaned from scientific research papers created by the startup. Richardson had identified a rheumatoid arthritis drug that might dampen some of the most severe effects of the COVID19 using the software.

Richardson and others at Benevolent AI¹¹ published two research papers laying out their hypothesis and supporting evidence in February. The research papers caught the attention of Eli Lilly, which markets the arthritis drug, known as baricitinib, under the brand name Olumiant.

Lilly announced this week that it is working with the US National Institute of Allergy and Infectious Diseases on a large clinical trial⁷of the drug in hospitalized COVID-19 patients.

The clinical trial should begin in the US this month and could expand to include patients in Europe and Asia. Results are expected as soon as late June where it usually takes years to design, organize, and launch a trial.

Clearview Facial Recognition Controversy

Clearview AI¹² quickly became one of the most elusive, secretive and reviled companies in the tech startup scene since it exploded onto the scene in January.

The #facialrecognition startup allows its law enforcement users to take a picture of a person, upload it and match it against its alleged database of 3 billion images, which the company scraped from public social media profiles. But a misconfigured server exposed the company’s internal files, apps and source code for anyone on the internet to find for a time.

The chief security officer at Dubai-based cybersecurity firm SpiderSilk, Mossab Hussein found the repository storing Clearview’s source code though the repository was protected with a password, a misconfigured setting allowed anyone to register as a new user to log in to the system storing the code.

The repository contained Clearview’s sourcecode, which could be used to compile and run the apps from scratch. Some of the company’s secret keys and credentials, which granted access to Clearview’s cloudstorage repository also stored in the repository. Clearview stored copies of its finished Windows, Mac, and Android apps, as well as its iOS app inside those buckets.

Facebook, Twitter, and YouTube have already filed cease-and-desist letters with Clearview AI.

AI Real Time detection of COVID-19

Artificial intelligence algorithm applied to more than 2,000 lung X-ray images is helping radiologists more quickly identify signs of early pneumonia in COVID19 patients developed by UC San Diego Health. Mayo Clinic¹³ teamed up with Minnesota’s health department to create an artificial intelligence-powered tool to identify greater COVID-19 transmission zones in southern Minnesota.

Tampa General Hospital using a new AI system to detect⁸ feverish visitors with a simple facial scan. The AI powered monitoring equipment Sheba Medical Center equipped two remote hospital units established to treat COVID19 patients.

The AI platform, Flu Sensemodel developed by researchers at University of Massachusetts Amherst was able to analyze coughing sounds and crowd size collected by the handheld device in real-time, then use that data to accurately predict daily illness rates in each clinic.

An Israeli startup company’s Vocalis Health¹⁴ working with hospitals and academic institutions to sample voices of confirmed coronavirus patients through a mobile application.

Baptist Health based in Kentucky using an AI platform from remote-patient-monitoring startup Current Health Ltd. to track about 20 COVID-19 patients.

Ethical challenges in AI systems

Top research labs in the US, Europe, and researchers from Google Brain, Intel, OpenAI forces this week to release what the group calls a toolbox for turning AI ethics principles into practice.

The idea of paying developers for finding bias in #AI, akin to the bug bounties offered in security software is the kit for organizations creating AImodels. To ensure AI is made with public trust and societal well-being in mind recommendation and other ideas detailed in a preprint paper published this week. Developers could unearth more bias than is revealed by measures in place.

JB Rubinovitz initially suggested the idea of bias bounties for AI in 2018. Google said it has paid $21 million to security bug finders, while bug bounty platforms like HackerOne¹⁵ and Bugcrowd¹⁶ have raised funding rounds in recent months.

Regina Dugan, former director, DARPA advocated red-teaming exercises to address ethical challenges in AI systems. A team led primarily by prominent Google AI ethics researchers released a framework for internal use at organizations.

More than 80 organizations — including #OpenAI, Google, and even the US military — have drafted AI ethics principles, but this paper asserts AI ethics principles⁹ are only a first step to beneficial societal outcomes from AI.

Do descriptive texts help AI to generalize across dynamic environments?

Scientists at the University of Toronto and the Vector Institute, an independent nonprofit dedicated to advancing AI, propose BabyAI++, a platform to study whether descriptive texts help AI to generalize across dynamic environments in a new study published this week on the preprint server Arxiv. Several baseline models along with this one will soon be available on GitHub.

Reinforcement learning- one of the most powerful techniques in machine learning, which entails spurring software agents toward goals via rewards. It requires a large number of compute cycles to complete as sample inefficient, and it adapts poorly to environments that differ from the training environment without additional data to cover variations.

BabyAI++ was designed to test the theory that prior knowledge of tasks through structured language could be combined with #reinforcementlearning to mitigate its shortcomings. The platform builds upon an existing reinforcement learning framework to generate various dynamic, color tile-based environments along with texts that describe their layouts in detail.

The tasks are randomly generated like the environments themselves, and they’re communicated to the agent through “Baby-Language,” a compositional language that uses a subset of English vocabulary.

Enterprise AI and ML Technology Implementation

AI and ML are the most cutting-edge technologies used by businesses. The commercial providers entering the arena for adopting either of these solutions is no longer excessive. Even small enterprises can implement relevant AI and ML technologies with careful budgeting and a detailed plan.

Here are 8 valuable tips for companies to successfully incorporate AI/ML on a budget-

✅ Identify The Problem You’re Trying To Solve-

It is important to identify the actual problem that AI/ML can solve.

✅ Start With Non-Core Functions

✅ Identify How You Will Measure Success

It is important to define what success looks like.

✅ Start Small, Gain Momentum, Grow

✅ Get support from your team-

You need as much support from your team as possible to integrate AI with your daily operations.

✅ Look At Proven Use Cases-

The easiest way to start is to look at proven AI/ML use cases where the value has already been shown inside your industry.

✅ Develop A Risk Management Strategy-

AI can perform complex, multivariate analysis, helping to find a signal in the noise that allows a business to maximize risk reduction with minimal resource and effort.

✅ Ensure You Have Well-Defined Data-

Ensure you have extremely well-defined data and rules that can be used as the basis to build out an AI or ML system.

Works Cited

¹AI Ethical Principles, ²Crunching Data, ³Deployment of AI Capabilities, ⁴Repetitive Tasks, ⁵Experimenting with AI, ⁶Predictive Models, ⁷Clinical Trials, ⁸AI System, ⁹AI Ethics Principles

Companies Cited

¹⁰Diagnostic Robotics, ¹¹Benevolent AI, ¹²Clearview AI, ¹³Mayo Clinic, ¹⁴Vocalis Health, ¹⁵HackerOne, ¹⁶Bugcrowd