Years ago, the thought of using software to fight a deadly pathogen might have seemed far-fetched. Today, its a reality. The Coronavirus pandemic has caused monumental shifts in the use and deployment of artificial intelligence (AI) around the world.

Of those now using AI to fight Coronavirus, none are more prominent than China. From software that diagnoses the symptoms of Coronavirus to algorithms that identify and compile data on individuals with high temperatures vis--vis infrared cameras, China is showcasing the potential applications of AI. But Beijing is also demonstrating its willingness to leverage the technology to solve many of its problems.

To understand the potential benefits and perils, we need to delve a bit deeper into the subject of AI itself. Artificial intelligence essentially falls into two categories: narrow and general. Narrow AI is a type of machine learning that is limited to specifically defined tasks, while general AI refers to totally autonomous intelligence akin to human cognition. General AI remains a distant dream for many, but the real-world implications of narrow AI exist in the presentand China is working diligently to become a world leader in it.

In his book AI Superpowers: China, Silicon Valley, and the New World Order, former Microsoft executive and Google China president Kai-Fu Lee describes how the country began rapid development of AI as a response to AlphaGo, a software program that successfully bested the worlds top player in the ancient game of Go back in 2017. That victory, Lee explains, showcased China's Communist Party (CCP) research and technology with infinite potential.

The revelation was a sea-change. In its 2019 Annual Report, the U.S.-China Economic and Security Review Commission noted that the Next Generation AI Development Plan released in 2017 by Chinas State Council marked a shift in Chinas approach to AI, from pursuing specific applications to prioritizing AI as foundational to overall economic competitiveness.

The results have been rapidand pronounced. China is still considered to be second in the race to AI (behind the U.S.), but it is quickly gaining traction. As the United Nations World Intellectual Property Organization (WIPO) noted last year, China leads in AI-related publications and patent applications originating from public research institutions, and the gap is shrinking between the U.S. and China in patent requests originating from the private sector.

And because the aggregation of vast swathes of data is what drives the most effective artificial intelligence, China is in a unique position to persevere. With the worlds largest population and close to no data privacy protections, the PRC has the potential to develop the worlds best AI products.

Beijing is also working hard to maintain its freedom of action in this domain. Back in March, China triedand nearly succeededin installing its candidate as head of the WIPO, a move that would essentially have assured that its lengthy track record of violating intellectual property rights, theft and espionage would not come with any consequences.

Those practices are already raising international hackles. In April of 2020, Bloomberg reported that electric carmaker Tesla is now seeking further legal action to analyze the source code of a competitors product in China after a former Tesla employee allegedly left the company in 2018 for the Chinese startup, carrying with him secrets from Teslas self-driving AI, AutoPilot.

But the CCP is also harnessing AI to strengthen its authoritarian state. Against the backdrop of the coronavirus pandemic, the Chinese government has stepped up its repressive domestic practices, including its persecution and detention of Uyghur Muslims in Western China and a broad crackdown on Hong Kong. Worryingly, Chinese advances in AI seem to be empowering these practices, as well as making them more effective.

These dynamics should matter a great deal to the United States, which has stepped up its strategic competition with China in earnest in recent months. Chinas activism on the AI front, and its attention to this emerging technology, has made abundantly clear that the PRC places tremendous value on dominating the field of AI. Washington should think deeply about what that would mean, in both a political and a technological sense. And then it should get just as serious in this sphere as well.

Ryan Christensen is a researcher at the American Foreign Policy Council in Washington, DC.

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China Wants to Lead the World on AI. What Does That Mean for America? - The National Interest

Here and elsewhere, youve likely read many articles and studies on the potentially transformative effect of artificial intelligence and machine learning on the workplace. Weve seen some of that transformation unfold more quickly during the ongoing COVID-19 pandemic, as workplaces across sectors explore automation to ensure essential processes, from security checks to invoice payments, keep happening.

There are concerns that AI and ML will cause significant job losses, and it seems inevitable that they will change or even eliminate some kinds of positions. But to unlock the potential of AI and ML for the enterprise, existing job roles must be filled and new ones must be created. Below are 10 workplace roles that could emerge as AI/ML continues to advance and organizations continue to integrate the technology into their operations.

1. Knowledge manager: As part of its Project Cortex rollout, Microsoft wants companies to hire knowledge managers. These employees would be responsible for the quality of knowledge shared across an organization and aggregating a companywide taxonomy.

2. AI scientist: Some organizations, of course, have just such a role in place already but as artificial intelligence becomes increasingly powerful and adopted by more and more organizations, these specialists will become essential to a growing number of companies.

3. AI manager: And of course, if you are adding AI scientists and other AI/ML experts to your team, someone with knowledge and experience in that field needs to manage them and help them work together. Management staffers with specific experience in artificial intelligence and machine learning could become increasingly important in integrating these technologies across an organization.

4. Subject matter expert: Also recommended by Microsoft in relation to Project Cortex, an organizations subject matter experts would have a deep understanding of how information is organized in the areas under their purview. As Microsoft imagines it, the one in this role would work closely with the knowledge manager.

5. Personality designer: Behind every AI architecture is a personality that someone had to design. Think of Siri, for example. Someone decided what the responses would be like, how the voice would sound, etc. As virtual assistants powered by machine learning become an increasingly common part of our work (and home) lives, the work of these designers and related workers, like writers and UI/UX professionals will be in even more demand.

6. AI trainer: The underlying structures of AI and ML products and services must be trained and trained well to be effective. That can be done with machines, but its likely to be far more effective if a human is selecting the information with an eye to effectiveness and bias.

7. Content services administrator: In some cases, a content services or knowledge administrator would represent an expansion of an existing role, like a SharePoint or Teams administrator. But this IT professional would set up and run knowledge product suites, like Cortex, Microsoft hopes.

8. Intelligence ethicist: The world is increasingly grappling with ethical issues brought forward by AI and ML, from built-in bias to spurious or even dangerous or illegal applications of technology. Large firms, in particular, will need intelligence ethicists to guide the decisions made by the products and services they are developing.

9. Data detective: Have you been impressed by the work done by COVID-19 contact tracers or intrigued by the possibilities (and pitfalls) of location tracing apps? Work as a data detective could be in your future. These employees could use data points for example, the locations someone has visited to solve problems, create datasets for AI/ML training, and develop new products and services.

10. Data broker: AI- and ML-driven technologies require reams of data to learn from, and that data has to come from somewhere. A data broker would be in charge of accessing, managing and deploying that data for an organization. Its a role likely to become increasingly complex as more and more jurisdictions add data-centric regulations like the California Consumer Privacy Act.

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10 Jobs That Should Emerge to Help Enterprises Advance AI and ML - ITPro Today

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Researchers from Googles DeepMind and the University of Oxford recommend that AI practitioners draw on decolonial theory to reform the industry, put ethical principles into practice, and avoid further algorithmic exploitation or oppression.

The researchers detailed how to build AI systems while critically examining colonialism and colonial forms of AI already in use in a preprint paper released Thursday. The paper was coauthored by DeepMind research scientists William Isaac and Shakir Mohammed and Marie-Therese Png, an Oxford doctoral student and DeepMind Ethics and Society intern who previously provided tech advice to the United Nations Secretary Generals High-level Panel on Digital Cooperation.

The researchers posit that power is at the heart of ethics debates and that conversations about power are incomplete if they do not include historical context and recognize the structural legacy of colonialism that continues to inform power dynamics today. They further argue that inequities like racial capitalism, class inequality, and heteronormative patriarchy have roots in colonialism and that we need to recognize these power dynamics when designing AI systems to avoid perpetuating such harms.

Any commitment to building the responsible and beneficial AI of the future ties us to the hierarchies, philosophy, and technology inherited from the past, and a renewed responsibility to the technology of the present, the paper reads. This is needed in order to better align our research and technology development with established and emerging ethical principles and regulation, and to empower vulnerable peoples who, so often, bear the brunt of negative impacts of innovation and scientific progress.

The paper incorporates a range of suggestions, such as analyzing data colonialism and decolonization of data relationshipsand employing the critical technical approach to AI development Philip Agre proposed in 1997.

The notion of anticolonial AI builds on a growing body of AI research that stresses the importance of including feedback from people most impacted by AI systems. An article released in Nature earlier this week argues that the AI community must ask how systems shift power and asserts that an indifferent field serves the powerful. VentureBeat explored how power shapes AI ethics in a special issue last fall. Power dynamics were also a main topic of discussion at the ACM FAccT conference held in early 2020 as more businesses and national governments consider how to put AI ethics principles into practice.

The DeepMind paper interrogates how colonial features are found in algorithmic decision-making systems and what the authors call sites of coloniality, or practices that can perpetuate colonial AI. These include beta testing on disadvantaged communities like Cambridge Analytica conducting tests in Kenya and Nigeria or Palantir using predictive policing to target Black residents of New Orleans. Theres also ghost work, the practice of relying on low-wage workers for data labeling and AI system development. Some argue ghost work can lead to the creation of a new global underclass.

The authors define algorithmic exploitation as the ways institutions or businesses use algorithms to take advantage of already marginalized people and algorithmic oppression as the subordination of a group of people and privileging of another through the use of automation or data-driven predictive systems.

Ethics principles from groups like G20 and OECD feature in the paper, as well as issues like AI nationalism and the rise of the U.S. and China as AI superpowers.

Power imbalances within the global AI governance discourse encompasses issues of data inequality and data infrastructure sovereignty, but also extends beyond this. We must contend with questions of who any AI regulatory norms and standards are protecting, who is empowered to project these norms, and the risks posed by a minority continuing to benefit from the centralization of power and capital through mechanisms of dispossession, the paper reads. Tactics the authors recommend include political community action, critical technical practice, and drawing on past examples of resistance and recovery from colonialist systems.

A number of members of the AI ethics community, from relational ethics researcher Abeba Birhane to Partnership on AI, have called on machine learning practitioners to place people who are most impacted by algorithmic systems at the center of development processes. The paper explores concepts similar to those in a recent paper about how to combat anti-Blackness in the AI community, Ruha Benjamins concept of abolitionist tools, and ideas of emancipatory AI.

The authors also incorporate a sentiment expressed in an open letter Black members of the AI and computing community released last month during Black Lives Matter protests, which asks AI practitioners to recognize the ways their creations may support racism and systemic oppression in areas like housing, education, health care, and employment.

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DeepMind researchers propose rebuilding the AI industry on a base of anticolonialism - VentureBeat

Social media companies, especially Twitter, have long faced criticism for how they flag speech and decide which accounts to ban. The underlying problem almost always has to do with the algorithms that they use to monitor online posts. Artificial intelligence systems are far from perfect when it comes to this task, but there is work constantly being done to improve them.

Included in that work is a new study coming out of the University of Southern California that attempts to reduce certain errors that could result in racial bias.

One of the issues that doesnt receive as much attention has to do with algorithms that are meant to stop the spread of hateful speech but actually amplify racial bias. This happens when the algorithms fail to recognize context and end up flagging or blocking tweets from minority groups.

The biggest problem with the algorithms in regard to context is that they are oversensitive to certain group-identifying terms like black, gay, and transgender. The algorithms consider these hate speech classifiers, but they are often used by members of those groups and the setting is important.

In an attempt to resolve this issue of context blindness, the researchers created a more context-sensitive hate speech classifier. The new algorithm is less likely to mislabel a post as hate speech.

The researchers developed the new algorithms with two new factors in mind: the context in regard to the group identifiers, and whether there are also other features of hate speech present in the post, like dehumanizing language.

Brendan Kennedy is a computer science Ph.D. student and co-lead author of the study, which was published on July 6 at ACL 2020.

We want to move hate speech detection closer to being ready for real-world application, said Kennedy.

Hate speech detection models often break, or generate bad predictions, when introduced to real-world data, such as social media or other online text data, because they are biased by the data on which they are trained to associate the appearance of social identifying terms with hate speech.

The reason the algorithms are oftentimes inaccurate is that they are trained on imbalanced datasets with extremely high rates of hate speech. Because of this, the algorithms fail to learn how to handle what social media actually looks like in the real world.

Professor Xiang is an expert in natural language processing.

It is key for models to not ignore identifiers, but to match them with the right context, said Ren.

If you teach a model from an imbalanced dataset, the model starts picking up weird patterns and blocking users inappropriately.

To test the algorithm, the researchers used a random sample of text from two social media sites that have a high-rate of hate speech. The text was first hand-flagged by humans as prejudiced or dehumanizing. The state-of-the-art model was then measured against the researchers own model for inappropriately flagging non-hate speech, through the use of 12,500 New York Times articles with no hate speech present. While the state-of-the-art models were able to achieve 77% accuracy in identifying hate vs non-hate, the researchers model was higher at 90%.

This work by itself does not make hate speech detection perfect, that is a huge project that many are working on, but it makes incremental progress, said Kennedy.

In addition to preventing social media posts by members of protected groups from being inappropriately censored, we hope our work will help ensure that hate speech detection does not do unnecessary harm by reinforcing spurious associations of prejudice and dehumanization with social groups.

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New Study Attempts to Improve Hate Speech Detection Algorithms - Unite.AI

Artificial intelligence (AI) presents an opportunity to transform how we allocate credit and risk, and to create fairer, more inclusive systems. AIs ability to avoid the traditional credit reporting and scoring system that helps perpetuate existing bias makes it a rare, if not unique, opportunity to alter the status quo. However, AI can easily go in the other direction to exacerbate existing bias, creating cycles that reinforce biased credit allocation while making discrimination in lending even harder to find. Will we unlock the positive, worsen the negative, or maintain the status quo by embracing new technology?

This paper proposes a framework to evaluate the impact of AI in consumer lending. The goal is to incorporate new data and harness AI to expand credit to consumers who need it on better terms than are currently provided. It builds on our existing systems dual goals of pricing financial services based on the true risk the individual consumer poses while aiming to prevent discrimination (e.g., race, gender, DNA, marital status, etc.). This paper also provides a set of potential trade-offs for policymakers, industry and consumer advocates, technologists, and regulators to debate the tensions inherent in protecting against discrimination in a risk-based pricing system layered on top of a society with centuries of institutional discrimination.

AI is frequently discussed and ill defined. Within the world of finance, AI represents three distinct concepts: big data, machine learning, and artificial intelligence itself. Each of these has recently become feasible with advances in data generation, collection, usage, computing power, and programing. Advances in data generation are staggering: 90% of the worlds data today were generated in the past two years, IBM boldly stated. To set parameters of this discussion, below I briefly define each key term with respect to lending.

Big data fosters the inclusion of new and large-scale information not generally present in existing financial models. In consumer credit, for example, new information beyond the typical credit-reporting/credit-scoring model is often referred to by the most common credit-scoring system, FICO. This can include data points, such as payment of rent and utility bills, and personal habits, such as whether you shop at Target or Whole Foods and own a Mac or a PC, and social media data.

Machine learning (ML) occurs when computers optimize data (standard and/or big data) based on relationships they find without the traditional, more prescriptive algorithm. ML can determine new relationships that a person would never think to test: Does the type of yogurt you eat correlate with your likelihood of paying back a loan? Whether these relationships have casual properties or are only proxies for other correlated factors are critical questions in determining the legality and ethics of using ML. However, they are not relevant to the machine in solving the equation.

What constitutes true AI is still being debated, but for purposes of understanding its impact on the allocation of credit and risk, lets use the term AI to mean the inclusion of big data, machine learning, and the next step when ML becomes AI. One bank executive helpfully defined AI by contrasting it with the status quo: Theres a significant difference between AI, which to me denotes machine learning and machines moving forward on their own, versus auto-decisioning, which is using data within the context of a managed decision algorithm.

Americas current legal and regulatory structure to protect against discrimination and enforce fair lending is not well equipped to handle AI. The foundation is a set of laws from the 1960s and 1970s (Equal Credit Opportunity Act of 1974, Truth in Lending Act of 1968, Fair Housing Act of 1968, etc.) that were based on a time with almost the exact opposite problems we face today: not enough sources of standardized information to base decisions and too little credit being made available. Those conditions allowed rampant discrimination by loan officers who could simply deny people because they didnt look credit worthy.

Today, we face an overabundance of poor-quality credit (high interest rates, fees, abusive debt traps) and concerns over the usage of too many sources of data that can hide as proxies for illegal discrimination. The law makes it illegal to use gender to determine credit eligibility or pricing, but countless proxies for gender exist from the type of deodorant you buy to the movies you watch.

Americas current legal and regulatory structure to protect against discrimination and enforce fair lending is not well equipped to handle AI.

The key concept used to police discrimination is that of disparate impact. For a deep dive into how disparate impact works with AI, you can read my previous work on this topic. For this article, it is important to know that disparate impact is defined by the Consumer Financial Protection Bureau as when: A creditor employs facially neutral policies or practices that have an adverse effect or impact on a member of a protected class unless it meets a legitimate business need that cannot reasonably be achieved by means that are less disparate in their impact.

The second half of the definition provides lenders the ability to use metrics that may have correlations with protected class elements so long as it meets a legitimate business need,andthere are no other ways to meet that interest that have less disparate impact. A set of existing metrics, including income, credit scores (FICO), and data used by the credit reporting bureaus, has been deemed acceptable despite having substantial correlation with race, gender, and other protected classes.

For example, consider how deeply correlated existing FICO credit scores are with race. To start, it is telling how little data is made publicly available on how these scores vary by race. The credit bureau Experian is eager to publicize one of its versions of FICO scores by peoples age, income, and even what state or city they live in, but not by race. However, federal law requires lenders to collect data on race for home mortgage applications, so we do have access to some data. As shown in the figure below, the differences are stark.

Among people trying to buy a home, generally a wealthier and older subset of Americans, white homebuyers have an average credit score 57 points higher than Black homebuyers and 33 points higher than Hispanic homebuyers. The distribution of credit scores is also sharply unequal: More than 1 in 5 Black individuals have FICOs below 620, as do 1 in 9 among the Hispanic community, while the same is true for only 1 out of every 19 white people. Higher credit scores allow borrowers to access different types of loans and at lower interest rates. One suspects the gaps are even broader beyond those trying to buy a home.

If FICO were invented today, would it satisfy a disparate impact test? The conclusion of Rice and Swesnik in their law review article was clear: Our current credit-scoring systems have a disparate impact on people and communities of color. The question is mute because not only is FICO grandfathered, but it has also become one of the most important factors used by the financial ecosystem. I have described FICO as the out of tune oboe to which the rest of the financial orchestra tunes.

New data and algorithms are not grandfathered and are subject to the disparate impact test. The result is a double standard whereby new technology is often held to a higher standard to prevent bias than existing methods. This has the effect of tilting the field against new data and methodologies, reinforcing the existing system.

Explainability is another core tenant of our existing fair lending system that may work against AI adoption. Lenders are required to tell consumers why they were denied. Explaining the rationale provides a paper trail to hold lenders accountable should they be engaging in discrimination. It also provides the consumer with information to allow them to correct their behavior and improve their chances for credit. However, an AIs method to make decisions may lack explainability. As Federal Reserve Governor Lael Brainard described the problem: Depending on what algorithms are used, it is possible that no one, including the algorithms creators, can easily explain why the model generated the results that it did. To move forward and unlock AIs potential, we need a new conceptual framework.

To start, imagine a trade-off between accuracy (represented on the y-axis) and bias (represented on the x-axis). The first key insight is that the current system exists at the intersection of the axes we are trading off: the graphs origin. Any potential change needs to be considered against the status-quonot an ideal world of no bias nor complete accuracy. This forces policymakers to consider whether the adoption of a new system that contains bias, but less than that in the current system, is an advance. It may be difficult to embrace an inherently biased framework, but it is important to acknowledge that the status quo is already highly biased. Thus, rejecting new technology because it contains some level of bias does not mean we are protecting the system against bias. To the contrary, it may mean that we are allowing a more biased system to perpetuate.

As shown in the figure above, the bottom left corner (quadrant III) is one where AI results in a system that is more discriminatory and less predictive. Regulation and commercial incentives should work together against this outcome. It may be difficult to imagine incorporating new technology that reduces accuracy, but it is not inconceivable, particularly given the incentives in industry to prioritize decision-making and loan generation speed over actual loan performance (as in the subprime mortgage crisis). Another potential occurrence of policy moving in this direction is the introduction of inaccurate data that may confuse an AI into thinking it has increased accuracy when it has not. The existing credit reporting system is rife with errors: 1 out of every 5 people may have material error on their credit report. New errors occur frequentlyconsider the recent mistake by one student loan servicer that incorrectly reported 4.8 million Americans as being late on paying their student loans when in fact in the government had suspended payments as part of COVID-19 relief.

The data used in the real world are not as pure as those model testing. Market incentives alone are not enough to produce perfect accuracy; they can even promote inaccuracy given the cost of correcting data and demand for speed and quantity. As one study from the Federal Reserve Bank of St. Louis found, Credit score has not acted as a predictor of either true risk of default of subprime mortgage loans or of the subprime mortgage crisis. Whatever the cause, regulators, industry, and consumer advocates ought to be aligned against the adoption of AI that moves in this direction.

The top right (quadrant I) represents incorporation of AI that increases accuracy and reduces bias. At first glance, this should be a win-win. Industry allocates credit in a more accurate manner, increasing efficiency. Consumers enjoy increased credit availability on more accurate terms and with less bias than the existing status quo. This optimistic scenario is quite possible given that a significant source of existing bias in lending stems from the information used. As the Bank Policy Institute pointed out in its in discussion draft of the promises of AI: This increased accuracy will benefit borrowers who currently face obstacles obtaining low-cost bank credit under conventional underwriting approaches.

One prominent example of a win-win system is the use of cash-flow underwriting. This new form of underwriting uses an applicants actual bank balance over some time frame (often one year) as opposed to current FICO based model which relies heavily on seeing whether a person had credit in the past and if so, whether they were ever in delinquency or default. Preliminary analysis by FinReg Labs shows this underwriting system outperforms traditional FICO on its own, and when combined with FICO is even more predictive.

Cash-flow analysis does have some level of bias as income and wealth are correlated with race, gender, and other protected classes. However, because income and wealth are acceptable existing factors, the current fair-lending system should have little problem allowing a smarter use of that information. Ironically, this new technology meets the test because it uses data that is already grandfathered.

That is not the case for other AI advancements. New AI may increase credit access on more affordable terms than what the current system provides and still not be allowable. Just because AI has produced a system that is less discriminatory does not mean it passes fair lending rules. There is no legal standard that allows for illegal discrimination in lending because it is less biased than prior discriminatory practices. As a 2016 Treasury Department study concluded, Data-driven algorithms may expedite credit assessments and reduce costs, they also carry the risk of disparate impact in credit outcomes and the potential for fair lending violations.

For example, consider an AI that is able, with a good degree of accuracy, to detect a decline in a persons health, say through spending patterns (doctors co-pays), internet searches (cancer treatment), and joining new Facebook groups (living with cancer). Medical problems are a strong indicator of future financial distress. Do we want a society where if you get sick, or if a computer algorithm thinks you are ill, that your terms of credit decrease? That may be a less biased system than we currently have, and not one that policymakers and the public would support. Of all sudden what seems like a win-win may not actually be one that is so desirable.

AI that increases accuracy but introduces more bias gets a lot of attention, deservedly so. This scenario represented in the top left (quadrant II) of this framework can range from the introduction of data that are clear proxies for protected classes (watch Lifetime or BET on TV) to information or techniques that, on a first glance, do not seem biased but actually are. There are strong reasons to believe that AI will naturally find proxies for race, given that there are large income and wealth gaps between races. As Daniel Schwartz put it in his article on AI and proxy discrimination: Unintentional proxy discrimination by AIs is virtually inevitable whenever the law seeks to prohibit discrimination on the basis of traits containing predictive information that cannot be captured more directly within the model by non-suspect data.

Proxy discrimination by AI is even more concerning because the machines are likely to uncover proxies that people had not previously considered.

Proxy discrimination by AI is even more concerning because the machines are likely to uncover proxies that people had not previously considered. Think about the potential to use whether or not a person uses a Mac or PC, a factor that is both correlated to race and whether people pay back loans, even controlling for race.

Duke Professor Manju Puri and co-authors were able to build a model using non-standard data that found substantial predictive power in whether a loan was repaid through whether that persons email address contained their name. Initially, that may seem like a non-discriminatory variable within a persons control. However, economists Marianne Bertrand and Sendhil Mullainathan have shown African Americans with names heavily associated with their race face substantial discrimination compared to using race-blind identification. Hence, it is quite possible that there is a disparate impact in using what seems like an innocuous variable such as whether your name is part of your email address.

The question for policymakers is how much to prioritize accuracy at a cost of bias against protected classes. As a matter of principle, I would argue that our starting point is a heavily biased system, and we should not tolerate the introduction of increased bias. There is a slippery slope argument of whether an AI produced substantial increases in accuracy with the introduction of only slightly more bias. Afterall, our current system does a surprisingly poor job of allocating many basic credits and tolerates a substantially large amount of bias.

Industry is likely to advocate for inclusion of this type of AI while consumer advocates are likely to oppose its introduction. Current law is inconsistent in its application. Certain groups of people are afforded strong anti-discrimination protection against certain financial products. But again, this varies across financial product. Take gender for example. It is blatantly illegal under fair lending laws to use gender or any proxy for gender in allocating credit. However, gender is a permitted use for price difference for auto insurance in most states. In fact, for brand new drivers, gender may be the single biggest factor used in determining price absent any driving record. America lacks a uniform set of rules on what constitutes discrimination and what types of attributes cannot be discriminated against. Lack of uniformity is compounded by the division of responsibility between federal and state governments and, within government, between the regulatory and judicial system for detecting and punishing crime.

The final set of trade-offs involve increases in fairness but reductions in accuracy (quadrant IV in the bottom right). An example includes an AI with the ability to use information about a persons human genome to determine their risk of cancer. This type of genetic profiling would improve accuracy in pricing types of insurance but violates norms of fairness. In this instance, policymakers decided that the use of that information is not acceptable and have made it illegal. Returning to the role of gender, some states have restricted the use of gender in car insurance. California most recently joined the list of states no longer allowing gender, which means that pricing will be more fair but possibly less accurate.

Industry pressures would tend to fight against these types of restrictions and press for greater accuracy. Societal norms of fairness may demand trade-offs that diminish accuracy to protect against bias. These trade-offs are best handled by policymakers before the widespread introduction of this information such as the case with genetic data. Restricting the use of this information, however, does not make the problem go away. To the contrary, AIs ability to uncover hidden proxies for that data may exacerbate problems where society attempts to restrict data usage on the grounds of equity concerns. Problems that appear solved by prohibitions then simply migrate into the algorithmic world where they reappear.

The underlying takeaway for this quadrant is one in which social movements that expand protection and reduce discrimination are likely to become more difficult as AIs find workarounds. As long as there are substantial differences in observed outcomes, machines will uncover differing outcomes using new sets of variables that may contain new information or may simply be statistically effective proxies for protected classes.

The status quo is not something society should uphold as nirvana. Our current financial system suffers not only from centuries of bias, but also from systems that are themselves not nearly as predictive as often claimed. The data explosion coupled with the significant growth in ML and AI offers tremendous opportunity to rectify substantial problems in the current system. Existing anti-discrimination frameworks are ill-suited to this opportunity. Refusing to hold new technology to a higher standard than the status quo results in an unstated deference to the already-biased current system. However, simply opening the flood gates under the rules of can you do better than today opens up a Pandoras box of new problems.

The status quo is not something society should uphold as nirvana. Our current financial system suffers not only from centuries of bias, but also from systems that are themselves not nearly as predictive as often claimed.

Americas fractured regulatory system, with differing roles and responsibilities across financial products and levels of government, only serves to make difficult problems even harder. With lacking uniform rules and coherent frameworks, technological adoption will likely be slower among existing entities setting up even greater opportunities for new entrants. A broader conversation regarding how much bias we are willing to tolerate for the sake of improvement over the status quo would benefit all parties. That requires the creation of more political space for sides to engage in a difficult and honest conversation. The current political moment in time is ill-suited for that conversation, but I suspect that AI advancements will not be willing to wait until America is more ready to confront these problems.

The Brookings Institution is a nonprofit organization devoted to independent research and policy solutions. Its mission is to conduct high-quality, independent research and, based on that research, to provide innovative, practical recommendations for policymakers and the public. The conclusions and recommendations of any Brookings publication are solely those of its author(s), and do not reflect the views of the Institution, its management, or its other scholars.

Microsoft provides support to The Brookings InstitutionsArtificial Intelligence and Emerging Technology (AIET) Initiative, and Apple, Facebook, and IBM provide general, unrestricted support to the Institution. The findings, interpretations, and conclusions in this report are not influenced by any donation. Brookings recognizes that the value it provides is in its absolute commitment to quality, independence, and impact. Activities supported by its donors reflect this commitment.

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Reducing bias in AI-based financial services - Brookings Institution

When governments the world over announced lockdowns, the hunt for best collaboration and video-calling apps had begun for most users. There were video calling apps for funHousepartyand then there were business apps. But competition in the space was limited. Zoom captured a large share of the market with its user interface and accessible features. The disaster that followed in terms of the company trying to keep up with demand and buying Chinese servers gave space to the likes of Microsoft and Google to add more users. But as work from home becomes a norm, and people get attuned to living with video calling apps, companies are incorporating more features to keep their userbase. One of the biggest highlights for all apps has been the use of artificial intelligence and machine learning to attract users. The latest addition to this is Microsoft.

What has Microsoft introduced?Microsoft last week announced features that will help users enable team mode, where they can sit together in a different environment. So, with a virtual background, you can see everyone sitting right in front of you in a classroom, library or coffee house setting, thereby making the whole experience more personal. Microsoft is also trying to incorporate a feature which allows you to adjust brightness and other parameters of the video.

How is it different from virtual backgrounds?Zoom has had virtual backgrounds for long now. Microsoft is a late entrant, but the concept is the same. When Zoom uses virtual background, it often renders the depth of the image to superimpose other backgrounds on it. The machine-learning algorithm then identifies the human component and changes the rest. The technology is not perfect, adjust the camera too fast, and it will not work. In this case, Microsoft is using the same technology to extract you from the image and put you in the same room along with friends and colleagues sitting behind a desk or a table. That way you can see all the participants in one window.

What is Google Meet doing?Google is using AI differently. Instead of using it for video, it is using the technology to cut out background noise. This active noise filtering means that you can only hear the sound of the speaker, and every other sound gets muzzled.

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Teaming with AI: How Microsoft is taking on Zoom on virtual background front - The Financial Express

Theres no shortage of promises when it comes to AI. Some say it will solve all problems while others warn it will bring about the end of the world as we know it. Both positions regularly play out in Hollywood plotlines like Westworld, Carbon Black, Minority Report, Her, and Ex Machina. Those stories are compelling because they require us as creators and consumers of AI technology to decide whether we trust an AI system or, more precisely, trust what the system is doing with the information it has been given.

This content was produced by Nuance. It was not written by MIT Technology Review's editorial staff.

Joe Petro is CTO at Nuance.

Those stories also provide an important lesson for those of us who spend our days designing and building AI applications: trust is a critical factor for determining the success of an AI application. Who wants to interact with a system they dont trust?

Even as a nascent technology AI is incredibly complex and powerful, delivering benefits by performing computations and detecting patterns in huge data sets with speed and efficiency. But that power, combined with black box perceptions of AI and its appetite for user data, introduces a lot of variables, unknowns, and possible unintended consequences. Hidden within practical applications of AI is the fact that trust can have a profound effect on the users perception of the system, as well as the associated companies, vendors, and brands that bring these applications to market.

Advancements such as ubiquitous cloud and edge computational power make AI more capable and effective while making it easier and faster to build and deploy applications. Historically, the focus has been on software development and user-experience design. But its no longer a case of simply designing a system that solves for x. It is our responsibility to create an engaging, personalized, frictionless, and trustworthy experience for each user.

The ability to do this successfully is largely dependent on user data. System performance, reliability, and user confidence in AI model output is affected as much by the quality of the model design as the data going into it. Data is the fuel that powers the AI engine that virtually converts the potential energy of user data into kinetic energy in the form of actionable insights and intelligent output. Just as filling a Formula 1 race car with poor or tainted fuel would diminish performance, and the drivers ability to compete, an AI system trained with incorrect or inadequate data can produce inaccurate or unpredictable results that break user trust. Once broken, trust is hard to regain. That is why rigorous data stewardship practices by AI developers and vendors are critical for building effective AI models as well as creating customer acceptance, satisfaction, and retention.

Responsible data stewardship establishes a chain of trust that extends from consumers to the companies collecting user data and those of us building AI-powered systems. Its our responsibility to know and understand privacy laws and policies and consider security and compliance during the primary design phase. We must have a deep understanding of how the data is used and who has access to it. We also need to detect and eliminate hidden biases in the data through comprehensive testing.

Treat user data as sensitive intellectual property (IP). It is the proprietary source code used to build AI models that solve specific problems, create bespoke experiences, and achieve targeted desired outcomes. This data is derived from personal user interactions, such as conversations between consumers and call agents, doctors and patients, and banks and customers. It is sensitive because it creates intimate, highly detailed digital user profiles based on private financial, health, biometric, and other information.

User data needs to be protected and used as carefully as any other IP, especially for AI systems in highly regulated industries such as health care and financial services. Doctors use AI speech, natural-language understanding, and conversational virtual agents created with patient health data to document care and access diagnostic guidance in real time. In banking and financial services, AI systems process millions of customer transactions and use biometric voiceprint, eye movement, and behavioral data (for example, how fast you type, the words you use, which hand you swipe with) to detect possible fraud or authenticate user identities.

Health-care providers and businesses alike are creating their own branded digital front door that provides efficient, personalized user experiences through SMS, web, phone, video, apps, and other channels. Consumers also are opting for time-saving real-time digital interactions. Health-care and commercial organizations rightfully want to control and safeguard their patient and customer relationships and data in each method of digital engagement to build brand awareness, personalized interactions, and loyalty.

Every AI vendor and developer not only needs to be aware of the inherently sensitive nature of user data but also of the need to operate with high ethical standards to build and maintain the required chain of trust.

Here are key questions to consider:

Who has access to the data? Have a clear and transparent policy that includes strict protections such as limiting access to certain types of data, and prohibiting resale or third-party sharing. The same policies should apply to cloud providers or other development partners.

Where is the data stored, and for how long? Ask where the data lives (cloud, edge, device) and how long it will be kept. The implementation of the European Unions General Data Protection Regulation, the California Consumer Privacy Act, and the prospect of additional state and federal privacy protections should make data storage and retention practices top of mind during AI development.

How are benefits defined and shared? AI applications must also be tested with diverse data sets to reflect the intended real-world applications, eliminate unintentional bias, and ensure reliable results.

How does the data manifest within the system? Understand how data will flow through the system. Is sensitive data accessed and essentially processed by a neural net as a series of 0s and 1s, or is it stored in its original form with medical or personally identifying information? Establish and follow appropriate data retention and deletion policies for each type of sensitive data.

Who can realize commercial value from user data? Consider the potential consequences of data-sharing for purposes outside the original scope or source of the data. Account for possible mergers and acquisitions, possible follow-on products, and other factors.

Is the system secure and compliant? Design and build for privacy and security first. Consider how transparency, user consent, and system performance could be affected throughout the product or service lifecycle.

Biometric applications help prevent fraud and simplify authentication. HSBCs VoiceID voice biometrics system has successfully prevented the theft of nearly 400 million (about $493 million) by phone scammers in the UK. It compares a persons voiceprint with thousands of individual speech characteristics in an established voice record to confirm a users identity. Other companies use voice biometrics to validate the identities of remote call center employees before they can access proprietary systems and data. The need for such measures is growing as consumers conduct more digital and phone-based interactions.

Intelligent applications deliver secure, personalized, digital-first customer service. A global telecommunications company is using conversational AI to create consistent, secure, and personalized customer experiences across its large and diverse brand portfolio. With customers increasingly engaging across digital channels, the company looked to technology partners to expand its own in-house expertise while ensuring it would retain control of its data in deploying a virtual assistant for customer service.

A top-three retailer uses voice-powered virtual assistant technology to let shoppers upload photos of items theyve seen offline, then presents items for them to consider buying based on those images.

Ambient AI-powered clinical applications improve health-care experiences while alleviating physician burnout. EmergeOrtho in North Carolina is using the Nuance Dragon Ambient eXperience (DAX) application to transform how its orthopedic practices across the state can engage with patients and document care. The ambient clinical intelligence telehealth application accurately captures each doctor-patient interaction in the exam room or on a telehealth call, then automatically updates the patient's health record. Patients have the doctors full attention while streamlining the burnout-causing electronic paperwork physicians need to complete to get paid for delivering care.

AI-driven diagnostic imaging systems ensure that patients receive necessary follow-up care. Radiologists at multiple hospitals use AI and natural language processing to automatically identify and extract recommendations for follow-up exams for suspected cancers and other diseases seen in X-rays and other images. The same technology can help manage a surge of backlogged and follow-up imaging as covid-19 restrictions ease, allowing providers to schedule procedures, begin revenue recovery, and maintain patient care.

As digital transformation accelerates, we must solve the challenges we face today while preparing for an abundance of future opportunities. At the heart of that effort is the commitment to building trust and data stewardship into our AI development projects and organizations.

See more here:

Beyond the AI hype cycle: Trust and the future of AI - MIT Technology Review

Lead author Ernest Lee, MD, PhD, and colleagues found many studies in the recent literature focused on image analysis and classification of skin lesionsno surprise since digital photography is by now ubiquitous in the field.

Here they comment that machine learning is a natural fit for translation into dermatology because dermatology is a specialty that is heavily reliant on visual evaluation and pattern recognition.

However, the researchers also found machine learning is being applied to everything from studying the genetic basis of skin diseases to identifying associations between comorbidities, and to designing and predicting patient responses to drug therapies.

The simultaneous rise of machine learning and next-generation sequencing in particular represents a golden opportunity to advance precision dermatology, and multidisciplinary collaborations between machine learning experts, biologists and dermatologists will be required to expand the scope of this research, Lee and co-authors write.

Read the original post:

Dermatology researchers: AI tools soon to be 'tightly integrated into daily clinical practice' - AI in Healthcare

The photo has been used for illustrative purpose.

It has been oft-quoted albeit humouredly that the ideal of medicine is the elimination of the physician. The emergence and encroachment of artificial intelligence (AI) on the field of medicine, however, puts an inconvenient truth on the aforementioned witticism. Over the span of their professional lives, a pathologist may review 100,000 specimens, a radiologist more so; AI can perform this undertaking in days rather than decades.

Visualise your last trip to an NHS hospital, the experience was either one of romanticism or repudiation: the hustle and bustle in the corridors, or the agonising waiting time in A&E; the empathic human touch, or the dissatisfaction of a rushed consultation; a seamless referral or delays and cancellations.

Contrary to this, our experience of hospitals in the future will be slick and uniform; the human touch all but erased and cleansed, in favour of complete and utter digitalisation. Envisage an almost automated hospital: cleaning droids, self-portered beds, medical robotics. Fiction of today is the fact of tomorrow, doesnt quite apply in this situation, since all of the above-mentioned AI currently exists in some form or the other. But then, what comes of the antiquated, human doctor in our future world? Well, they can take consolation, their unemployment status would be part of a global trend: the creation displacing the creator. Mechanisation of the workforce leading to mass unemployment. This analogy of our friend, the doctor, speaks volumes; medicine is cherished for championing human empathy if doctors arent safe, nobody is. The solution: socialism.

Open revolt against machinery seems a novel concept set in some futuristic dystopian land, though, the reality can be found in history: the Luddites of Nottinghamshire. A radical faction of skilled textile workers protecting their employment through machine destruction and riots, during the industrial revolution of the 18th century. The now satirised term Luddite, may be more appropriately directed to your fathers fumbled attempt at unlocking his iPhone, as opposed to a militia.

What lessons are to be learnt from the Luddites? Much. Firstly, the much-fictionalised fight for dominance between man and machine is just that: fictionalised. The real fight is within mankind. The Luddites fight was always against the manufacturer, not the machine; machine destruction simply acted as the receptacle of dissidence. Secondly, government feeling towards the Luddites is exemplified through 12,000 British soldiers being deployed against the Luddites, far exceeding the personnel deployed against Napoleons forces in the Iberian Peninsula in the same year.

Though providing clues, the future struggle against AI and its wielders will be tangibly different from that of the Luddite struggle of the 18th century, next; its personal, its about soul. Our higher cognitive faculties will be replaced: the diagnostic expertise of the doctor, decision-making ability of the manager, and (if were lucky) political matters too.

The monopolising of AI will lead to mass unemployment and mass welfare, reverberating globally. AI efficiency and efficacy will soon replace the error-prone human. It must be the case that AI is to be socialised and the means of production, the AI, redistributed: in other words, brought under public ownership. Perhaps, the emergence of co-operative groups made up of experienced individuals will arise to undertake managerial functions in their previous, now automated, workplace. Whatever the structure, such an undertaking will require the full intervention of the state; on a moral basis not realised in the Luddite struggle.

Envisaging an economic system of nationalised labour of AI machinery performing laborious as well as lively tasks shant be feared. This economic model, one of abundance, provides a platform of the fullest of creative expression and artistic flair for mankind. Humans can pursue leisurely passions. Imagine the doctor dedicating superfluous amounts of time on the golfing course, the manager pursuing artistic talents. And what of the politician? Well, thats anyones guess

An abundance economy is one of sustenance rather than subsistence; initiating an old form of socialism fit for a futuristic age. AI will transform the labour market by destroying it; along with the feudalistic structure inherent to it.

Thought-provoking questions do arise: what is to become of human aspiration? What exactly will it mean to be human in this world of AI?

Ironically; perhaps it will be the machine revolution that gives us the resolution to age-old problems in society.

More:

AI technology will soon replace error-prone humans all over the world but here's why it could set us all free - Gulf Today

Many electric vehicle companies are enabling modern technologies like Artificial Intelligence and IoT in their vehicles

AI and IoT have transformed the entire delivery services especially with the electric vehicles

The implication of AI and IoT in electric vehicles ensure efficiency and safety

Urban logistics and delivery services are one of the main issues of every big and small city. From grocery to food items to everything, the delivery market has grown rapidly with the growth of technology and the Internet. It moves vehicles in rush hours and on roads which are already congested by private traffic.

According to the data of MDS Transmodal Limited, the impact of delivery services is that they represent between 8 and 18% of urban traffic flows and they decrease by 30% the road capacity because of pick-up and deliveries operations and it continues to grow in the coming years. Delivery operations have a high impact on congestion and urban environmental quality. They are responsible for about 25% of CO2 mobility emissions in urban areas.

A new venture that has joined the delivery services is that Electric Vehicles. The electric vehicle industry is growing rapidly to combat pollution. Electric vehicles (EVs) is seen as a catalyst to the reduction of CO2 emissions and more intelligent mode of transportation systems. The Government of India is also pushing for a shift towards electric vehicles for every purpose. The Indian government has claimed that India will move to 30% electric vehicles by 2030.

The Government of India has the vision of making the country electrically mobile. The government of India has encouraged mainstream electric mobility by dedicating INR 10,000 Cr to boost EV usage under Faster Adoption and Manufacturing of Hybrid and Electric Vehicles (FAME) II scheme and a 5% reduction of GST on electric vehicles.

As the technology is growing and many industries are adopting the changes, many electric vehicle companies are enabling modern technologies like Artificial Intelligence and IoT in their vehicles. They are providing these e-scooters for many purposes, from personal use to now in the smart delivery ecosystem.

Use of e-bikes, e-cargo bikes and e-scooters is extremely positive for the enhancement of Corporate Social Responsibility (CSR), visibility and green image among customers and clients, cost savings because it consumes low energy and it is low maintenance and performances are very good. It is easy to access any location in urban areas and reliability is too high with these e-vehicles. This is the reason, nowadays more delivery giants are opting for e-scooters instead of petrol or diesel scooters.

Some problems are related to the usage of electric vehicles like the lack of adequate charging stations, limited autonomy especially in hilly areas and some technical malfunctions of engines and batteries. But the AI and IoT technologies have even come with the solution to all these problems.

AI and IoT have transformed the entire delivery services especially with the electric vehicles (EVs). Now, the electric scooters of the delivery executives are AI and IoT enabled. So that the drivers behaviour can be monitored for safe and timely delivery of goods. Companies have started using Telematics devices for tracking & monitoring vehicle movement during the delivery. These technologies will not only monitor the movement of vehicles but also ensure the safety of drivers in case of any kind of road accidents.

Using AI and IoT, it will be easy to contact the driver and a consumer in case of an emergency. These scooters can be controlled by a mobile application, GPS which are installed on the vehicles and an accelerometer can tell the company every single movement of a scooter during the delivery of the goods.

Using the AI and IoT, e-scooters which are equipped with cellular, GPS, and accelerometer technology, they use machine learning to interpret the habits of their riders and either notify dangerous habits of the drivers or alter their machines to produce safer conditions. Artificial Intelligence has now made it possible for the driver to look at the app after delivery and see where they went, how fast they drove, if they made any dangerous moves, and also give tips for a safer delivery next time.

Attachment of an accelerometer to a scooter with AI and IoT, also made it possible for the company or consumer to see when a rider accelerates too quickly or brakes too sharply. Electric vehicles also come with features like navigation assist, ride statistics, remote diagnostics, voice-enabled app, anti-theft alarm and lock, speedometer call alerts, ride behaviour-based artificial intelligence suggestions, which can be used in case of emergency. AI and IoT have helped the electric scooter to connect the drivers smartphone and store all vehicle-related data on the cloud.

Next level of tech revolution can be seen in the electric vehicle sector. There is 247 connectivity to a cloud server which allows a user to monitor the performance of the vehicle even when the driver is not around. Data analytic algorithms employed by the server analyses the data and notifies the user about possible service needs.

Modern technologies like AI and IoT have also improved the battery charging technology of Electric Vehicles (EV) and reduced the time it takes to stop at a gas station. It is the result of that Electric Vehicles companies are using artificial intelligence to monitor the state of the battery as it is charging. This improvement in battery technology has not only made delivery services faster but also safe for the consumers as well as delivery companies.

See the rest here:

Implication Of AI And IoT Enabled Electric Scooters For Smart Delivery Services - Inc42 Media

A new study from West Virginia University (WVU) Rockefeller Neuroscience Institute (RNI) uses a wearable device and artificial intelligence (AI) to predict COVID-19 up to 3 days before symptoms occur. The study has been an impressive undertaking involving over 1000 health care workers and frontline workers in hospitals across New York, Philadelphia, Nashville, and other critical COVID-19 hotspots.

The implementation of the digital health platform uses a custom smartphone application coupled with an ura smart ring to monitor biometric signals such as respiration and temperature. The platform also assesses psychological, cognitive, and behavioral data through surveys administered through a smartphone application.

We know that wearables tend to suffer from a lack of accuracy, particularly during activity. However, the ura ring appears to take measurements while the user is very still, especially during sleep. This presents an advantage as the accuracy of wearable devices greatly improves when the user isnt moving. RNI noted that the ura ring has been the most accurate device they have tested.

Given some of the early warning signals for COVID-19 are fever and respiratory distress, it would make sense that a device able to measure respiration and temperature could be used as an early detector of COVID-19. In fact, weve seen a few wearable device companies attempt much of what RNI is doingas well as a few DIY attempts. RNIs study has probably been the most thorough work released so far, but were sure that many more are upcoming.

The initial phase of the study was deployed among healthcare and frontline workers but is now open to the general public. Meanwhile the National Basketball Association (NBA) is coordinating its re-opening efforts using uras technology.

We hope to see more results emerge from RNIs very important work. Until then, stay safe Hackaday.

See the original post here:

Detect COVID-19 Symptoms Using Wearable Device And AI - Hackaday

This detailed and well synchronized research report about the AI Governance market is the most significant, up-to-date, ready-to-refer research analysis that allows readers to draw substantial market specific cues that eventually remain crucial growth influencers in the AI Governance market , more specifically under the influence of COVID-19 implications that have visibly impacted normal industry process in multiple ways, leaving a trail of tangible implications.

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Global AI Governance Market is accounted for xx USD million in 2019 and is expected to reach xx USD million by 2025 growing at a CAGR of xx% : IBM,...

Swedish supermarket Coop Sweden has a retail grocery assistant on its websites. Cooper, as the assistant is called, can help you with dietary requirements, suggest recipes and provide nutritional information. The idea behind Cooper is to increase interaction with consumers while providing a seamless shopping experience.

As consumers move online, implementing the technologies of the fourth industrial revolution (4IR) is becoming more crucial. Cooper is an example of the 4IR in practice. These technologies are changing the way we work, commute, communicate and, as Cooper will tell you, even shop. The 4IR is based on high-level technology such as artificial intelligence, automation, biotechnology, nanotechnology and communication technologies that permeates society. It is a combination of various technologies that can communicate with humans and interact with other devices and programs.

The lockdown necessitated by the Covid-19 pandemic has been an important yardstick for understanding behavioural changes in consumers as online options become more commonplace. A recent Nielsen study found that 37% of South Africans say they are shopping more online in this period. As Gareth Paterson, a lead retail analyst at Nielsen South Africa, put it, Amid the strange new world of Covid-19, online grocery shopping has been a lifeline for many South African consumers who have desperately sought out safe and secure shopping alternatives amidst the uncertainty of lockdown living. As a result, available online shopping platforms, especially for groceries, medicines, and other necessary items, have seen a surge in usage over the last few weeks as consumers prefer not to venture into stores and have increasingly opted for these reduced touchpoint alternatives.

According to data from the survey, Nielsen is anticipating that options such as click and collect and online personal shopping will grow exponentially, resulting in prolonged behavioural changes. Retailers have been quick to cotton on to this shift and have responded in innovative and effective ways. For instance, Checkers has launched an app called Checkers Sixty60, which has groceries delivered to you in 60 minutes. There are 5 000 groceries to choose from and options to substitute products if your first choice is not available.

In various industries, the coronavirus has been an important lesson where we are well equipped to deal with the 4IR and where we still have gaps. This will undoubtedly signal a shift in consumer behaviour and many will not return to traditional brick-and-mortar retail. We will increasingly see more retailers adapt to this way of operating. In fact, a report by global management consultancy Accenture last year suggested that South African retailers would see a knock to a business if they did not embrace e-commerce. The emphasis on traditional stores, Accenture argues, means that many retailers are losing out on the potential profits that come with online offerings. Yet, interestingly enough, the current pandemic may subvert this.

This is not to say that online shopping has not had somewhat of a watershed moment in recent years. Perhaps the best example that provides a holistic user experience is the Mr Price app. With it, you can shop online, find the stock in stores and even upload a picture of something you like for it to suggest similar items available on the app through the snap and shop feature. For instance, I could either take or upload a picture of a pair of brown formal shoes that I saw a colleague wear. The app will then pull any stock available at Mr Price that looks similar and provide a list of suggestions accompanied by pictures.

The starkest instance of the popularity of online retail is Black Friday, which has gained popularity in South Africa in the last few years. It is probably the biggest day of the year for retailers, particularly online retailers. In the week leading up to it, consumers receive hordes of massive Black Friday discounts. Some of them may have put together wish lists to check out at the stroke of midnight while others may have used their phones to search for discounts.

AI is tailoring the online experience and it is determining prices, inventory and making distribution far more efficient for your favourite retailers. Another example of this on Instagram is the move to online shopping with a new AR shopping feature that is being rolled out, which allows consumers to try on products digitally before buying them. For example, using your phone you could try on the latest shade of Mac lipstick to see how you would look. This followed a rollout of a checkout feature that allowed you to buy products directly on Instagram without ever leaving the app.

The try-on feature is limited to certain brands and is still in a trial phase, but it is as easy to use as the filters when you create a story that could give you dog ears and a tongue or freckles and blue eyes. The long-term vision is to roll this out with all retail, so, for example, you could see what a couch looks like in your living room. This is not the only technology Instagram has adopted. AI influencers have been introduced, which have been surprisingly popular.

According to consumer insight website LendEDU, three years ago 52.9% of millennials said Instagram has the most influence on them when making shopping decisions. For instance, many followers use the website LIKEtoKNOW.it, which sends a direct link to a product after a shopper likes a post. Creating completely digital influencers is a whole new avenue. Miquela is an AI influencer with 2.4-million followers. Just like any other influencer, her posts are perfectly planned, she has a themed feed, has sponsored content and gives her followers useful advice and brand recommendations. But she does not actually exist she is run with AI technology. This has not stopped her career from taking off.

Last year, she collaborated with Prada for Milan Fashion Week by posting 3D-generated gifs of herself at the Milan show venue wearing the spring/summer 2018 collection. On Pradas Instagram account, she gave their followers a mini-tour of the space, just like any influencer would for a brand. She is not an outlier there are many more like her. Balmain recently announced a Balmain Army made up entirely of computer-generated imagery (CGI) models. There is also a dedicated modelling agency for digital models called The Digital.

Amazon, the largest online retailer by revenue, has 45 000 robots at its warehouses to fulfil orders and a fleet of airborne drones into service for fast deliveries. It is not just online that retail is transforming with the 4IR. There is room to implement this kind of technology at brick-and-mortar level. The introduction of robotics has streamlined checkout processes, for instance. In the United Kingdom, you can self-checkout at grocery stores that weigh your goods to prevent theft. Similarly, there are robots akin to sales assistants in stores in the United States they can help you find an item either verbally or through the touch screen. Some robots can perform real-time inventory tracking.

Best Buy, the US-based electronics store, has an automated system much like the claw machine at the arcade that can retrieve products from shelves. There is scope to streamline and automate processes that will prove to be cost-effective for retailers in the long run. Accelerated adoption of technology will be a key strategic move that could lift retailers margins significantly. Retailers can introduce digital technologies and automation into their operations to reduce costs and enhance the customer experience. They can turn e-commerce from a threat to a growth opportunity, a McKinsey and Company report on the future of work in South Africa reads.

One of the grim realities of this era we are moving into is that there will be knock-on employment, particularly of low-skill workers. The caveat is that there will be demand for graduates and employees with higher skills levels, and we need to meet the demand for graduates not to fall into an even deeper unemployment crisis.

From a retail perspective, there is so much to be done that can augment consumers experiences. As industries vie to be a step ahead in the ever-changing context, consumers and business owners have to be open to these experiences and shifts. As physicist William Pollard once said: Without change there is no innovation, creativity, or incentive for improvement. Those who initiate change will have a better opportunity to manage the change that is inevitable.

Professor Tshilidzi Marwala is the vice-chancellor and principal of the University of Johannesburg and deputy chair of the Presidential Commission on the Fourth Industrial Revolution

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Cooper, the grocery assistant with AI, gives concierge service - Mail and Guardian

By Balakrishna DR

Globally, the financial services industry has proved to be an enthusiastic adopter of Artificial Intelligence (AI) driven by the availability of data and investment appetite. Creative implementation of AI by start-ups and fintechs has helped further this trend. From personalisation to customer service, fraud detection and prevention to compliance, and risk monitoring to intelligent contract documents, AI has helped banks gain better control and predictability.

Today, customers expect faster, personal, and meaningful services and interactions with their banks and little tolerance for generic unsolicited messages. Therefore, banks must leverage AI to balance the need for privacy and security with personalisation and engagement. That said, the Indian banking sector has some amount of catching up to do.

While Indian banks have explored the use of AI, it has primarily been used to improve customer experience by adding chatbots as an additional interface for customers like SIA by State Bank of India, Eva by HDFC and iPal by ICICI. State-owned banks have been slow to leverage AI, largely because AI implementation requires banks to operate outside of the traditional privacy framework. India still does not have robust data protection and privacy policy. Reserve Bank of India (RBI) needs to take a commanding and dynamic role in framing regulations on emerging technologies, data privacy and ensuring the business interests of the banks.

Banks must adopt new business models simultaneously to integrate AI into their strategic plans and explore the use of AI for analytics and to improve customer experience. However, reliance on legacy systems, lack of data science talent, and cost constraints have impeded seamless adoption of AI. They must focus on three key aspects:

Fraud detection: AI plays a vital role in fraud detection, given the heightened threat of cyberattacks. As per the 2019 RBI annual report, losses due to banking frauds have risen by a whopping 73.8% despite the Governments efforts to curb them. What is more alarming is that banks took an average of 22 months between the occurrence of fraud and its detection, as per RBI data. Considering RBIs zero-liability safety net in the event of cyber frauds, it is imperative banks adopt best-fit practices and technology levers to mitigate these risks. With adoption of real-time payments, there has also been rapid innovation in the digital fraud landscape.

Set against this backdrop, banks must deploy context-sensitive AI solutions to enable advanced and adaptive real-time monitoring of their payment networks. These AI solutions additionally leverage relevant data points to assess transaction risk, true identity-matching, and identification of complex typologies and patterns.

Digitisation of processes: The tremendous proliferation of mobile devices and the internet can be leveraged to enable the superior user experience and analytics-based functionalities that give consumers an insight into their spending patterns and provide recommendations on investment and risk profiles. For instance, digitising the KYC process to eliminate the need for physical document submission and verification is something that traditional banks still do not offer. This can be simplified by utilising AI-based computer vision technology to verify documents, Optical/Intelligent Character Recognition (OCR/ICR) technologies to digitise scanned documents, and Natural Language Processing (NLP) to make sense of them.

Decision making: AI is a great fit in areas where decisions are based on available structured and unstructured data. For example, it can help predict potential loan defaulters and offer loss mitigation strategies that will work for them. It can help determine the best time to approach a customer to sell a new product. AI-based smart environments can collate data from multiple sources and drive an inference and enable SMEs to take decisions. AI can also improve straight-through processing using Intelligent Automation to automate repetitive processes that need decision making.

Given the magnitude of the challenge, it might make sense for banks to come together to establish a consortium for knowledge sharing on AI. This would also help Indias numerous regional and cooperative banks that are behind on the technology curve. A consortium could help uplift these small banks and enable them to be integrated seamlessly into a broader nationwide secure banking network. Whichever way it happens, AI in Indian banking is only set to grow.

The author is Senior VP, Service Offering Head Energy, Communications, Services and AI & Automation Services, Infosys

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Banking on AI: The time is ripe for Indian banks to embrace artificial intelligence - The Financial Express

SALT LAKE CITY, July 10, 2020 (GLOBE NEWSWIRE) -- In a release issued under the same headline on June 1, 2020 by OMNIQ, Inc. (OTCQB:OMQS), please be advised that the second paragraph as originally issued contained certain inaccuracies, not related to financial results or projections, which have been corrected below.

OMNIQ, Inc. (OTCQB:OMQS) (OMNIQ or the Company), announces that it has been selected to deploy its Quest Shield campus safety solution at the Talmudical Academy of Baltimore in Maryland.

The Quest Shield security package uses the Companys AI-based SeeCube technology platform, a ground-breaking cloud-based/on-premise security solution for Safe Campus/School applications. The platform provides unique AI-based computer vision technology and software to gather real-time vehicle data, enabling the Quest Shield to identify and record images of approaching vehicles including color, make and license plate information. The license plate is then compared against the schools internal watch list to provide immediate notifications of unauthorized vehicles to security and administrative personnel. In addition to providing a vehicle identification and recognition solution to the Talmudical Academy, the Quest Shield comprehensive security platform addresses other security concerns including controlling access to the buildings and visitor management as well as the ability to pre-register guests for school activities.

Additionally, as part of COVID-19 mitigation, parents in Maryland will be asked to take and record their childs temperature each day before they leave for school. Quest Shield will automate this process, by providing parents an online form where they may record the temperature. All Talmud Academy students will be equipped with an ID tag that will have a QR code that can be read with a barcode scanner. As students enter campus, faculty equipped with Quest handheld scanners will read the barcode to confirm that the students temperature has been taken that day; if the form has not been filled in, faculty will check temperatures before allowing students inside.

Shai Lustgarten, CEO of OMNIQ, commented: It is our privilege to work with the Talmudical Academy to provide our solution to enhance safety at their Baltimore campus. Quest Shield is an extension of the homeland security solution we designed for the Israeli authorities to fight terrorism and save lives.

Rabbi Yaacov Cohen, Executive Director, Talmudical Academy of Baltimore, commented:Concern about campus safety and the safety of our students and faculty drove the Talmudical Academy to seek ways to implement new strategies aimed at preventing crimes and violence that may be committed on the school grounds. The unfortunate reality today is that situations we could never imagine just a few years ago are happening now with increasing regularity. Most security systems that are currently being deployed on other campuses are good at recording events subsequent to crimes being committed. With Quest Shield, we have an opportunity to alert personnel and Law Enforcement ahead of any sign of violence.

Mr. Lustgarten added: The Quest Shield has been tailored to provide a proactive solution to improve security and safety in schools and on campuses as well as community centers and places of worship in the U.S. that have unfortunately become a target for ruthless attacks. Were pleased to work with a forward-thinking organization like the Talmudical Academy, it is gratifying that the Academy selected the Quest Shield platform to strengthen its security precautions.

Additionally, many schools and communities are expressing concern around children returning to school in the fall due to COVID-19. With that in mind, Talmudical Academy will also employ the Quest Shield to provide an automated screening process to confirm that students have had their temperatures checked, per Maryland regulation, upon their arrival on campus and prior to them entering the school facilities.

Mr. Lustgarten concluded, We are proud to be able to improve student safety in the U.S., as well as in other vulnerable communities. Quest Shield has previously been implemented by a pre-K through Grade 12 school in Florida and at a Jewish Community Center in Salt Lake City. We look forward to working closely with the Academy and other institutions to promote the health and safety of students, faculty and support personnel.

About OMNIQ, Corp.OMNIQ Corp. (OMQS) provides computerized and machine vision image processing solutions that use patented and proprietary AI technology to deliver data collection, real time surveillance and monitoring for supply chain management, homeland security, public safety, traffic & parking management and access control applications. The technology and services provided by the Company help clients move people, assets and data safely and securely through airports, warehouses, schools, national borders, and many other applications and environments.

OMNIQs customers include government agencies and leading Fortune 500 companies from several sectors, including manufacturing, retail, distribution, food and beverage, transportation and logistics, healthcare, and oil, gas, and chemicals. Since 2014, annual revenues have grown to more than $50 million from clients in the USA and abroad.

The Company currently addresses several billion-dollar markets, including the Global Safe City market, forecast to grow to $29 billion by 2022, and the Ticketless Safe Parking market, forecast to grow to $5.2 billion by 2023.

Information about Forward-Looking StatementsSafe Harbor Statement under the Private Securities Litigation Reform Act of 1995. Statements in this press release relating to plans, strategies, economic performance and trends, projections of results of specific activities or investments, and other statements that are not descriptions of historical facts may be forward-looking statements within the meaning of the Private Securities Litigation Reform Act of 1995, Section 27A of the Securities Act of 1933 and Section 21E of the Securities Exchange Act of 1934.

This release contains forward-looking statements that include information relating to future events and future financial and operating performance. The words anticipate, may, would, will, expect, estimate, can, believe, potential and similar expressions and variations thereof are intended to identify forward-looking statements. Forward-looking statements should not be read as a guarantee of future performance or results, and will not necessarily be accurate indications of the times at, or by, which that performance or those results will be achieved. Forward-looking statements are based on information available at the time they are made and/or managements good faith belief as of that time with respect to future events, and are subject to risks and uncertainties that could cause actual performance or results to differ materially from those expressed in or suggested by the forward-looking statements. Important factors that could cause these differences include, but are not limited to: fluctuations in demand for the Companys products particularly during the current health crisis, the introduction of new products, the Companys ability to maintain customer and strategic business relationships, the impact of competitive products and pricing, growth in targeted markets, the adequacy of the Companys liquidity and financial strength to support its growth, the Companys ability to manage credit and debt structures from vendors, debt holders and secured lenders, the Companys ability to successfully integrate its acquisitions, and other information that may be detailed from time-to-time in OMNIQ Corp.s filings with the United States Securities and Exchange Commission. Examples of such forward looking statements in this release include, among others, statements regarding revenue growth, driving sales, operational and financial initiatives, cost reduction and profitability, and simplification of operations. For a more detailed description of the risk factors and uncertainties affecting OMNIQ Corp., please refer to the Companys recent Securities and Exchange Commission filings, which are available at http://www.sec.gov. OMNIQ Corp. undertakes no obligation to publicly update or revise any forward-looking statements, whether as a result of new information, future events or otherwise, unless otherwise required by law.

Investor Contact: John Nesbett/Jen BelodeauIMS Investor Relations203.972.9200jnesbett@institutionalms.com

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CORRECTION - OMNIQ's Artificial Intelligence-Based Quest Shield Solution Selected by the Talmudical Academy of Baltimore - GlobeNewswire

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