A Clemson University faculty member will use an award from the National Science Foundation (NSF) to examine middle school students’ data science knowledge and practices through the lens of social issues and gauge students’ sense of empowerment to positively change communities through data science.

Golnaz Arastoopour Irgens, assistant professor of learning sciences in the Clemson University College of Education, said it is a common misconception that data is neutral or free from the influence of social issues or that data has no effect on social issues. She said it is often the case that technology informed by data science, such as search engines or facial recognition software, has been shown to either reinforce discrimination or mischaracterize minority groups.

Because humans design these forms of technology and many more make decisions based on them, a critical eye on how they are developed and how they are utilized becomes necessary. Arastoopour Irgens said it follows that the way we educate students to employ data science and utilize it falls short when social, ethical and political issues are not integrated into that education.

“Data analytics education is not something new, but data analytics practices have changed dramatically and the decisions that are made based on these data are now affecting people at much larger scales than ever before,” Arastoopour Irgens said. “This can be a problem when the population of computer scientists is mainly white and male; the viewpoints of other, non-dominant populations aren’t represented, so it’s important for our youth to recognize and engage with the changes in this area.”

Arastoopour Irgens and a team of graduate education students will use a new methodology, quantitative ethnography, which uses statistical tools to make sense of what learners are saying and doing as they engage with data science practices. This methodology will allow her to develop visual, dynamic models of how learners are connecting data science knowledge and practices to social, ethical and political issues in ways that are meaningful to them.

In order to measure the degree to which students feel empowered to enact change through data science, Arastoopour Irgens will combine and adapt existing surveys that measure political perceptions, civic engagement and agency with those that measure computing confidence, enjoyment, perceived usefulness, motivation to succeed and identity/belongingness. The research team will then use statistical models to note any changes in empowerment or attitudes toward computing after participation in an after-school program, which is a central aspect of the research.

This program will take place at five different sites across Greenville County. Arastoopour Irgens will co-design the after-school program with after-school staff, families, youth and community members to ensure it is aligned with their communities’ interests and values.

“Although formal schooling is a place where critical data education should be taught, this project will focus on broader community engagement and design without the constraints of schooling,” Arastoopour Irgens said. “We have already started a partnership with the after-school center and have held meetings with staff and conducted short activities and interviews with youth. We are confident that this collaboration will result in some powerful learning experiences and impacts on youth and their communities.”

Arastoopour Irgens said that few programs currently engage learners at an early age in data science education in which culturally relevant social, ethical and political issues are the focus. The project aims to address this gap for an audience primarily comprised of children of color or those living in poverty—populations who are underserved and underrepresented in science, technology, engineering and math (STEM).

Arastoopour Irgens said the project serves underrepresented youth by providing culturally relevant experiences that may ultimately motivate them to pursue STEM majors and careers. Youth involved who may not be part of an underrepresented population will also be positively impacted as they are made more aware of harmful traditions of technology development and consumption that marginalize others.

“We plan to post all of our materials on my research lab website and make them openly available and easily adaptable for other educators to use,” Arastoopour Irgens said. “My hope is to continue this work after this initial trial and that other educators can improve upon what we’ve done and adapt it to better fit the needs of their learner populations.”

The research is funded by NSF’s Building Capacity in STEM Education Research, which supports projects that build individuals’ capacity to carry out high-quality STEM education research that will enhance the nation’s STEM education enterprise and broaden the pool of researchers that can conduct fundamental research in STEM learning and learning environments and broaden participation in STEM fields and enhance STEM workforce development.

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Knowledge is power and in today’s digital world, that knowledge is often documented in the form of data, which could be regarded as the world’s most valuable commodity. As such, exchange-traded fund (ETF) investors should be keen to capitalize on opportunities in big data.

“Big Data is shaping up to be the most valuable commodity in the world,” a Banyan Hill article said. “You may have heard this term thrown around before. Basically, Big Data is large data sets that are collected from digital and traditional sources. This could be things like information collected from your smartwatch, your Google Maps route to work, your Netflix viewing history, etc.”

“Companies use information like this to improve what they know about customers’ spending habits,” the article added. “Like oil, Big Data is a fuel. Businesses need data to innovate and to create new technology. Technologies need data to mature, scale, and remain operational. This is why Big Data will truly be the fuel that powers the Great American Reset.”

The use of big data won’t be relegated to just technology-focused companies. Its use will permeate into other sectors that can utilize big data to its fullest potential within their own respective business models.

“Precision medicine, e-commerce, manufacturing, logistics, and transportation are just a handful of the many industries that will get a boost from Big Data,” the article said.

Another fund to get exposure to disruption via data-driven technology is Goldman Sachs Motif Data-Driven World ETF (GDAT). The fund seeks to provide investment results that closely correspond to the performance of the Motif Data-Driven World Index, which is designed to deliver exposure to companies with common equity securities listed on exchanges in certain developed markets that may benefit from the on-going rapid increase in electronically recorded data in the world and its impact on the lifecycle of data delivery and processing.

GDAT essentially provides exposure to the beneficiaries of technological innovation, regardless of sector, geography, or market capitalization. They can be used individually or collectively to help investors position their portfolios for the future.

Another fund to consider is the SPDR S&P Kensho New Economies Composite ETF (KOMP). KOMP seeks to provide investment results that, before fees and expenses, correspond generally to the total return performance of the S&P Kensho New Economies Composite Index.

Under normal market conditions, the fund generally invests substantially all, but at least 80%, of its total assets in the securities comprising the index. The index is designed to capture companies whose products and services are driving innovation and transforming the global economy through the use of existing and emerging technologies, and rapid developments in robotics, automation, artificial intelligence, connectedness, and processing power.

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With the rapid growth of this world, there are so many fields to choose from. It’s not just engineering or medicine, there are many more options. With so many options given, there is one particular field which is quite interesting – data science. The term sounds familiar, but many people are confused about what exactly data science is. It is the most trending field in the tech industry and is highly demanded.

To begin with, data science is a mixture of many fields and concerns with data and is a huge part of artificial intelligence. It is a mixture of software, statistics and business analysis. It mostly comes under technology, but many other skills are needed to choose it as a career. The main job of a data scientist is to load the data, structure it, and find possible solutions.

The work of a data scientist is to first collect data, which is mostly machine fed data from various sources. He then sorts them and processes them, or rather segregates the data into categories. Generally, the data collected from various sources is common for many things and needs to be segregated and cleaned. The process is similar to cleaning your room. You remove the excess and useless garbage and keep the required things for further use. The process of segregation is called data cleaning. This cleaned data is then processed and is where the statistics come into the picture. The processed data is assembled into proper algorithms for further study and analysis. After this process, the data is used to come up with solutions to solve complex problems, which helps in maximizing the profits of that particular company.

As this field is multidisciplinary, there are many aspects which it involves like mathematics, technology and business. First comes mathematics, this mostly includes statistics and algebra and much more. Many people think that data science is only about statistics and that is false. Statistics is an important part of data science, but not the only thing required. Linear algebra is also important as it uses the algorithms used to process the data.

Next is technical knowledge, such as coding and hacking. It is a vital skill for a data scientist as most of the time he is in contact with data which can be processed by this very skill. To process this data, a data scientist must know his technical stuff as the data provided is enormous and dealing with the data is a tedious process where having great technical skills is a must. When it comes to hacking, it does not mean the word ‘hacking’ that we all have heard about like breaking into computers. It means the creativity in using those technical skills. This is the key quality a good data scientist must have. Python, R, SQL etc are mostly the coding languages used for data science.

And the last comes business acumen, which is also a vital skill for a good data scientist. The data processed needs to be used to solve complex problems to maximize profits. Mostly these problems are core business problems. Therefore, having a good business tactic is important.

Since data science is an emerging field, it has a huge demand. If you have all the above skills, data design is meant for you.

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Despite advancements in diagnostic, prognostic, and therapeutic processes there remain uncertainties regarding aspects of decision making that indicate new research on rare blood cancers, such as essential thrombocythemia (ET) and polycythemia vera (PV), need to be conducted, according to a paper published in Haematologica.

“Well organized observational and registry-based studies will play a key role in analyzing the clinical outcomes, hopefully with the help of a data mining approach and artificial intelligence techniques, as suggested by preliminary experiences in patients with PV treated with ruxolitinib,” wrote the authors.

ET and PV are subtypes of cancers a part of a larger group of related blood cancers called myeloproliferative neoplasms. ET is caused by an overproduction of blood platelets, leading to increased risk of thrombosis and other serious conditions. PV is caused by an overproduction of red blood cells, causing blood to be thick and flow slowly.

Regarding diagnostic criteria, the World Health Organization in 2016 introduced the concept that some patients with ET may actually have pre-fibrotic primary myelofibrosis (pre-PMF). Reference institutions speculate that about 15% to 30% of patients with ET could be reclassified as pre-PMF.

Studies have shown that the risk of blood clot development is similar between the 2; however, incidence of bleeding episodes is more frequent for patients with pre-PMF compared with patients with “true” ET. Survival rates were also different, ranging from 10.5 years to 14.7 years for patients with pre-PMF compared with 14.7 years to 21.8 years for patients with ET.

“Since expected survival is the key issue to discuss with a patient newly diagnosed with pre-PMF, acquiring such information definitely represents the most compelling unmet need in [patients with] pre-PMF,” wrote the authors.

Research for diagnostic and prognostic criteria may be most helpful and timely coming from conducting a study enrolling both incident and retrospective cases or by creating and making available a comprehensive clinical and biologic database.

The authors wrote that these approaches could help identify causes of death in patients with pre-PMF, rate of transformation to overt PMF and acute leukemia, and allow predictive variables to be identified.

Risk of thrombosis may change for patients with ET and PV as a result of gene mutations. In patients with ET, CALR gene mutations have been shown to correlate with lower thrombosis risks compared with patients with JAK2 V617F mutations. JAK2 V61F allele burdens may increase thrombosis risks in patients with PV as well; however, more data is needed to confirm whether genetic evaluation provides clinically relevant information.

Additionally, assessing the risk of thrombosis can be difficult as the distinction of patients at low risk is weakening. Patients treated with phlebotomy and low-dose aspirin (LDA) still exhibit a 2% annual rate of major thrombotic episodes, calling into question whether these treatment methods are appropriate.

The quality of data surrounding the use of LDA is also low despite being stemmed from a phase 3 trial in patients with PV and retrospective studies in patients with ET. New hypotheses are currently being tested to improve aspirin efficacy in thrombosis prevention.

Cytoreductive drugs may offer greater benefit than phlebotomies, however experts discouraged the use of cytoreductive drugs in young patients as there may be leukemogenic risks associated with currently available products, such as hydroyurea, which has shown efficacy in preventing arterial thromboses but may not prevent recurrent venous thromboembolism.

“We believe that young patients with no history of previous thrombosis could be exposed to cytoreductive treatment as long as they only receive drugs for which there is no evidence of promoting secondary leukemias or solid tumors,” explained the authors in response to seeing promising results from trials testing the effects of new cytoreductive drugs, interferon and Ropeginterferon ⍺-2ba.

The risk of transformation into acute myeloid leukemia (AML) for patients with PV or ET is estimated at 3% and 1%, respectively, after 10 years. Currently, there is no evidence that drugs such as hydroxyurea, interferon, anagrelide, or ruxolitinib can slow down transformation risks.

There is hope that new targeted drugs such as idasanutlin in patients with PV and givinostat in patients with PV or ET could reduce AML transformation risks. The effects other promising drugs including enasidenib and ivosidenib in combination with other forms of treatment, venetoclax, navitoclax, and CPX-351, are currently under investigation.

“At the moment, results from rigorously designed clinical trials in this category of high-risk patients are still lacking and the little interest…to promote studies in this setting represents a major problem. It is, therefore, a clear duty of the scientific community to promote academic trials for this unmet clinical need,” the authors wrote.

The post Authors Call for More Research on Rare Blood Cancers to Fill Knowledge Gaps appeared first on Artificial Intelligence.

Microsoft launched a private preview of “Project Cortex,” touted as a knowledge network for Microsoft 365 users, as part of this week’s Ignite conference in Orlando, Fla.

Project Cortex largely has its roots in SharePoint technology, as described by SharePoint team members in a podcast. An allusion to SharePoint’s role also was made by Jeff Teper, Microsoft’s corporate vice president of Office, SharePoint, OneDrive and Streams, in this Twitter post.

The idea of creating a knowledge management solution goes back to SharePoint’s origins, when it went by the “Tahoe” code name, according to Adam Harmetz, a partner group program manager at Microsoft.

“In the very first days of SharePoint, one of the first teams that created Tahoe was called ‘PKM,’ Publishing and Knowledge Management,” Harmetz said in the podcast.

Microsoft started Project Cortex as a way to extend Microsoft 365 collaboration capabilities, but the content was already in SharePoint Online.

“This whole things started a few years ago as, ‘What would we do if we wanted to create a new product extending the content collaboration of Microsoft 365?’ and the content of Microsoft 365 is SharePoint,” Harmetz added.

The idea of creating a knowledge management solution goes back to SharePoint’s origins, when it went by the “Tahoe” code name, according to Adam Harmetz, a partner group program manager at Microsoft.

“In the very first days of SharePoint, one of the first teams that created Tahoe was called ‘PKM,’ Publishing and Knowledge Management,” Harmetz said in the podcast.

Microsoft started Project Cortex as a way to extend Microsoft 365 collaboration capabilities, but the content was already in SharePoint Online.

“This whole things started a few years ago as, ‘What would we do if we wanted to create a new product extending the content collaboration of Microsoft 365?’ and the content of Microsoft 365 is SharePoint,” Harmetz added.

New Microsoft 365 Service
Microsoft announced Project Cortex on Monday at Ignite, describing it as the “first new service in Microsoft 365 since the launch of Microsoft Teams.” Behind it is the Microsoft Graph to find Microsoft 365-associated data within organizations, artificial intelligence (AI) to organize that information and SharePoint data repositories. Microsoft also worked with Microsoft Research in Cambridge and used its data mining technology in Project Cortex, according to Naomi Moneypenny, director of content services and insights at Microsoft, during the podcast.

In addition to using internal company data, Project Cortex has connectors for accessing external content, even in “third-party repositories,” via APIs. In that respect, connectors already exist for MediaWiki, Salesforce and ServiceNow solutions. Microsoft also has connectors built for its Windows File Share, SQL Database and Azure Data Lake Gen2 services.

End users of Microsoft 365 applications, such as Outlook, Microsoft Teams and Office, will experience Project Cortex via so-called “Topic Cards,” which pop up from text, showing organizational information. Harmetz described them as similar to People Cards, which already exist across Microsoft 365 services. A Topic Card gets created in the background using AI and metadata crunched by an enhanced Microsoft Managed Metadata Service. The card information is based on an organization’s “customers, products, projects, policies and procedures,” according to the announcement.

These processes and AI create a so-called “Knowledge Network” for organizations using Microsoft 365 solutions.

“Technically, AI is creating knowledge entities, a new object class, in the Microsoft Graph,” the announcement explained. “The relationships between those topics — those knowledge entities — and the experiences that connect this knowledge with people creates your knowledge network.”

The Managed Metadata Service automatically mines tags associated with content. It can also “import, export and integrate custom taxonomy with third party systems.”

Project Cortex end users also see Topic Pages, which display content selected by AI. They also have access to a “Knowledge Center” page, which offers a “personalized view of relevant knowledge across your organization.” The Knowledge Center page is based on SharePoint.

“These custom knowledge centers are built with and integrate seamlessly into your intelligent SharePoint intranet,” the announcement explained.

There are also Content Centers in Project Cortex, which are libraries that permit reporting and analysis processes using AI models. Those models might get built using Power Platform tools, such as “AI Builder and Power Automate which integrate with Cortex.” Power Automate is the new name for Microsoft Flow, a service that’s used to chain app actions and processes.

The security and compliance aspects in Project Cortex are similar to those of the Microsoft Graph, according to Moneypenny. She said that Microsoft’s services in Project Cortex are only mining the things that end users already have permissions to see. Topic Cards only appear if end users have access to those resources, she added.

Cortex Uses
Microsoft CEO Satya Nadella, during his Ignite keynote, suggested it’ll be possible for Project Cortex users to query Word documents using their voice. It might be used to surface details like contract payment due dates, for instance.

Moneypenny suggested that Project Cortex could be used for proposals management, helping organizations keep track of milestones and deliverables. It could also be used to handle forms processing by organizations, she added.

Project Cortex is currently available as a “private preview,” with limited sign-up to use it at this page. A broader release is planned for the “first half of 2020.”

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With more access to data and growing computing power, artificial intelligence (AI) is becoming increasingly powerful. But for it to be effective and meaningful, we must embrace people-first artificial intelligence strategies, according to Soumitra Dutta, professor of operations, technology, and information management at the Cornell SC Johnson College of Business. “There has to be a human agency-first kind of principle that lets people feel empowered about how to make decisions and how to use AI systems to support their decision-making,” notes Dutta. Knowledge@Wharton interviewed him at a recent conference on artificial intelligence and machine learning in the financial industry, organized in New York City by the SWIFT Institute in collaboration with Cornell’s SC Johnson College of Business.

In this conversation, Dutta discusses some myths around AI, what it means to have a people-first artificial intelligence strategy, why it is important, and how we can overcome the challenges in realizing this vision.

An edited transcript of the conversation follows.

Knowledge@Wharton: What are some of the biggest myths about AI, especially as they relate to financial services?

Soumitra Dutta: AI, as we all know, is not new per se. It has been there for as long as modern computing has been around, and it has gone through ups and downs. What we are seeing right now is an increased sense of excitement or hype. Some people would argue it’s over-hyped. I think the key issue is distinguishing between hope and fear. Today, what you read about AI is largely focused around fear — fear of job losses, fear of what it means in terms of privacy, fear of what it means for the way humans exist in society. The challenge for us is to navigate the fear space and move into the hope space. By “hope,” I mean that AI, like any other technology, has negative side effects – but it also presents enormous positive benefits. Our collective challenge is to be able to move into the positive space and look at how AI can help empower people, help them become better individuals, better human beings, and how that can lead to a better society.

Knowledge@Wharton: How do you get to the “hope” space in a way that is based on reality and away from the myths and hype?

Dutta: We need to have what I term as a “people-first” AI strategy. We have to use technology, not because technology exists, but because it helps us to become better individuals. When organizations deploy AI inside their work processes or systems, we have to explicitly focus on putting people first.

This could mean a number of things. There will be some instances of jobs getting automated, so we have to make sure that we provide adequate support for re-skilling, for helping people transition across jobs, and making sure they don’t lose their livelihoods. That’s a very important basic condition. But more importantly, AI provides tools for predicting outcomes of various kinds, but the actual implementation is a combination of the outcome prediction plus judgment about the outcome prediction. The judgment component should largely be a human decision. We have to design processes and organizations such that this combination of people and AI lets people be in charge as much as possible.

There has to be a human agency-first kind of principle that lets people feel empowered about how to make decisions, how to use AI systems to make better decisions. They must not feel that their abilities are being questioned or undercut. It’s the combination of putting people and technology together effectively that will lead to good AI use in organizations.

“The key issue is distinguishing between hope and fear…. The big challenge for us is to navigate the fear space and move into the hope space.”

Knowledge@Wharton: That’s an ambitious vision — of being people-centric in the way you think about AI. What are some of the challenges involved in realizing this vision?

Dutta: Some are technological challenges, and some are organizational challenges. In terms of technology, AI systems are broadly defined in two categories. First, there are the traditional ruled-based systems, systems that are based on if/then kinds of rules. These are much easier to integrate, partially because they can be explained logically, in human, understandable terms.

The second category, which is much more exciting — and which has had some of the most impressive results — involves the application of deep learning, neural networks, and other kinds of related technologies. These technologies, unfortunately, are still largely black boxes. It’s hard to explain the complex mathematics inside these boxes and why they come up with some outcomes and not others. Given the lack of transparency, it sometimes becomes hard for human beings to accept the outcome of the machines. Introducing more transparency into the prediction outcomes of AI systems is the technological challenge that many scientists around the world are trying to address.

The organizational challenges are equally complex, if not bigger. How do you design work systems and work processes that leverage the best of people and machines? This requires the ability to not just blindly follow the path of automation because it seems the most cost-effective way to handle things, but to also have the patience to understand how to redesign jobs. Jobs have to change as you implement AI systems inside organizations. That requires the ability to support people as they make transitions and to invest in their development and re-skilling.

AI systems, like many technological systems, provide additional support to people. This support has to make people feel more empowered. If systems don’t make people feel better about what they do, they will fail in terms of getting acceptance in the organization. So there are a lot of human issues and managerial issues involved in making sure that companies present a people-centric or a people-first approach to AI.

Knowledge@Wharton: If we look at the broad range of financial services and banking, in which sectors do you see the most disruption — or maybe the most innovation — through AI?

Dutta: The whole financial sector is ripe for the application of AI, because finance is extremely data-intensive. It has been on the forefront of technology applications. I would argue that AI could transform every single decision-making process in the finance sector because you have volumes and volumes of data. Traditionally, financial organizations primarily operated with only financial data. But now they are able to integrate social behavior as well as social media data. They can combine the human social side, the behavioral side, with the financial side. The complexity of data has increased tremendously, so how do you handle that kind of data complexity? AI has the best set of tools to handle this.

What I see at present is that financial organizations are experimenting. We’re trying to understand how to apply AI creatively and productively. One should not forget that this phase of applying AI in organizations is relatively new. Even in the case of leaders like Amazon and Google, it was only about seven or eight years ago that these organizations decided to focus in an important way on AI. There was a process of experimentation and building strength in R&D and also exploring what can make sense. That process of building big strength, of trying to explore ideas with AI, is only now starting in the financial sector. So we have not yet seen the full impact of AI in finance. We’re just starting out on this long path.

“It’s the combination of putting people and technology together effectively that will lead to good AI use in organizations.”

Knowledge@Wharton: There’s an interesting debate going on between how the U.S. or Western financial institutions are using AI relative to Chinese companies. In this competition between the U.S. and China, who do you think will take the lead in innovation and AI?

Dutta: It’s important to first understand what makes AI systems powerful. The general consensus is that AI technologies per se haven’t seen any massive breakthroughs. What has happened is much more data is available now for training AI systems. We also have much more computational power for running through different algorithms. There is also a lot more effort being spent on engineering.

Typically, when you build an AI system, it’s not a clean application where you write the algorithm and it works. Instead, you have to have 20 different models, try out 50 different data sets and look at different heuristics about what works. There is a combination of many approaches and a lot of testing that goes into obtaining an effective end outcome.

If you look at the elements that make for a successful AI system, what you see is that on data, China has a natural advantage because of its large population and the number of people doing online transactions. Chinese companies, at least the digital leaders, are sitting on enormous volumes of data that dwarf some of their American peers. This gives them an advantage. When it comes to computational resources, the U.S. has an edge in the design of advanced microprocessors and custom AI chips, though China has amassed a world-leading concentration of computing power. Engineering requires a lot of manpower to experiment, to build out systems and to try different variations. China has cheaper labor and also more manpower in terms of sheer numbers. So when we look at the three components, it’s quite likely that China is going to have an edge.

Data privacy is another big issue. In the U.S., there is some clarity on who owns the data and how it can or cannot be used. In China, that’s unclear as of now. Is it the company that owns the data? What kind of access does the government have to it? Who can use the data? So Chinese AI companies might have some challenges when it comes to their international growth.

Knowledge@Wharton: Is use of data an area where international regulations can play a role?

“Given the lack of transparency, it sometimes becomes hard for human beings to accept the outcome of the machines.”

Dutta: Yes and no. Things are moving so fast that regulators, in general, are behind. The European Union is probably the best-known example of a region trying to regulate data users. They’ve done some good work with the GDPR (General Data Protection Regulation). Some states like California are putting data privacy regulations in place. But I think it’s important to have a coordinated approach.

The ultimate goal should be that the customer owns the data and the customer should be able to decide who uses the data and under what conditions. But we are far away from that. The reality is that the large companies in the world — it doesn’t matter which part of the world they come from — have enormous power. Most people don’t read the fine print when they sign user agreements. The balance is tilted in favor of large private players. Regulation is behind, and customers don’t have the tools to manage the data themselves. We have a turbulent period ahead of us.

The big players — who have enormous data stores — will be reluctant to give it up because a lot of their competitive advantage is based on that. Unless there’s strong regulation or a vigorous consumer backlash, it’s not going to happen. I don’t see a strong consumer backlash happening because consumers are seduced by free applications and the convenience factor. Who’s going to give up Google search? Who’s going to give up other free services? People are increasingly accepting the fact that they’re losing control over their data in return for free services. So, regulation is probably the best-case scenario, but again, regulators have been relatively behind in most countries.

Knowledge@Wharton: One major question that keeps coming up about AI is what will happen to jobs — especially at the lower levels in organizations. How can the people question be dealt with in this regard?

Dutta: This is probably one of the biggest questions facing policy and society. What will be the impact of technology on jobs? If you look at the last 100 years, with the exception of the Great Depression in the U.S., the U.S. growth rate has been relatively constant, and unemployment has been relatively within a fixed range. Many people argue that technology has come and gone, but the U.S. industry has somehow adjusted. Yes, there have been shifts, people have lost jobs but they have also gained jobs. On the whole, employment has grown.

The big question in front of us today, to which we don’t have a clear answer, is what will happen with AI now? AI is different in the sense that it does not just look at automating — or potentially automating – lower-end jobs but also higher-end jobs. In medical domains, for example, many AI systems perform at a higher level than the best doctors.

Clearly, machines will do some jobs entirely or very substantially. We have to decide how to handle the people who are displaced. It is an issue of organizational leadership and policies, and of national initiatives and regulation. It is an issue of how you support the growth of new industries. If AI is allowing the growth of new industries, is the economy flexible and entrepreneurial enough to support that growth?

“If you have two partners who need to coexist, and one has some limits while the other does not have any limits, then how do you handle that merger?”

There are a lot of micro and macro issues in terms of supporting change, allowing new sectors to flourish, enabling people to learn new skills, and so on. That’s what makes the whole thing so complicated. It’s a challenge that we have to face collectively, because if you don’t get it right, there will be massive dislocations in society. The issues are solvable, but they have to be solved with collective action and determined leadership.

Knowledge@Wharton: If you gaze into your crystal ball, what do you see coming down the road?

Dutta: The next five to 10 years are going to be very important in determining how AI is used in society. The impact of AI will play out over several decades. That’s one reason why universities like Stanford are publicly committed to studying the impact of AI for the next 100 years. We have to start understanding what the possible implications are. In many cases, we don’t know what we don’t know about AI. What will the impact be? How will people react when systems become more intelligent, when they are capable of more autonomous learning? How will their behavior change? And especially if these machines are black boxes, how will we understand the interactions of the machines with human beings in society?

I don’t want to make it sound like science fiction, but it’s an important question ahead of us. Ultimately, we have to have people and machines coexisting in an effective manner. The challenge is that the machines’ capability is increasing — in some areas exceeding human beings — and potentially with no upper limits. That, I think, is the interesting challenge. If you have two partners who need to coexist, and one has some limits while the other does not, then how do you handle that merger?

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