Mediaboss Marketing

Many of us may not even understand exactly where or what the Cloud is.

Yet, much of the data and programs that control our lives live on this Cloud of distant computer servers with the directions to run our devices coming over the Internet.

As the prevalence of artificial intelligence (AI)-driven devices grows, researchers would like to bring some of that decision-making back to our own devices. WSU researchers have developed a novel framework to more efficiently use AI algorithms on mobile platforms and other portable devices. They presented their most recent work at the 2020 Design Automation Conference and the 2020 International Conference on Computer Aided Design.

The goal is to push intelligence to mobile platforms that are resource-constrained in terms of power, computation, and memory, said Jana Doppa, George and Joan Berry Associate Professor in the School of Electrical Engineering and Computer Science. This has a huge number of applications ranging from mobile health, augmented and virtual reality, self-driving cars, digital agriculture, and image and video processing mobile applications.

Voice-recognition software, mobile health, robotics, and Internet-of-Things devices all use artificial intelligence to keep society moving at an ever-faster and automated pace. Self-driving cars powered by AI algorithms remain somewhere on the not-too-distant horizon.

The decisions for these increasingly sophisticated devices are all made in the Cloud, but as demands increase, the Cloud can become increasingly problematic, Doppa said. For instance, it isnt fast enough. Having a device in a self-driving car decide to turn right while looking both ways requires that information go from the car to the Cloud and then back to the car.

The time required to make decisions might not meet real-time requirements, said Partha Pande, Boeing Centennial Chair professor in School of EECS, who collaborated in this research.

Many rural or under-developed areas also dont have easy access to the infrastructure needed for the requirements of AI related communications and transferring information back and forth through the Cloud can also raise privacy concerns.

At the same time, however, requiring sophisticated computer algorithms to run on portable devices is also problematic. Computational resources havent been good enough, a phones computing memory is small, and a lot of decision-making will quickly drain the battery power.

We need to run the algorithms in a resource-constrained environment, Pande said.

Doppas group came up with a framework that is able to run complex neural network-based algorithms locally using less power and computation.

The researchers took an approach that prioritizes problem solving. As in human decision-making in which problems vary in their complexity and require more or less brain power, the researchers developed a framework in which their algorithms spend a lot of energy on only the complex parts of problems while using less resources for the easy ones.

By doing this, we are improving performance and saving a lot of energy, Doppa said.

So, for instance, in a digital agriculture application, their more efficient software and hardware could be embedded on a UAV, which could efficiently make decisions about crop spraying with less computational and energy requirements.

The researchers have applied their algorithms to virtual/augmented reality as well as image editing applications. The researchers are the first to adapt state-of-the-art AI approaches for structured outputs to a mobile platform. These include Graph Convolution Networks (GCNs), which are used to produce three-dimensional object shapes from images in augmented and virtual reality, and Generative Adversarial Networks (GANs) technology, which is used to generate synthetic images. In the case of the GAN technology, the solution the researchers developed was able to achieve a more than 50% savings in energy for a loss of about 10% in accuracy.

Since mobile platforms are constrained by resources, there is a great need for low-overhead solutions for these emerging GCNs and GANs to perform energy-constrained inference, said Nitthilan Kanappan Jayakodi, a graduate student in the School of Electrical Engineering and Computer Science who was lead author on the research and was selected as a Richard Newton Young Fellow from the ACM Special Interest Group on Design Automation for his outstanding research contributions. To the best of our knowledge, this is the first work on studying methods to deploy emerging GCNs and GANs to predict complex structured outputs on mobile platforms.

The work was funded by the National Science Foundation and the U.S. Army Research Office.

Continue reading here:
Artificial intelligence on the edge | WSU Insider | Washington State University - WSU News

The increasing attention being paid to artificial intelligence raises important questions about its integration with social sciences and humanity, according to David De Cremer, founder and director of the Centre on AI Technology for Humankind at the National University of Singapore Business School. He is the author of the recent book, Leadership by Algorithm: Who Leads and Who Follows in the AI Era?

While AI today is good at repetitive tasks and can replace many managerial functions, it could over time acquire the general intelligence that humans have, he said in a recent interview with AIfor Business (AIB),a new initiative at Analytics at Wharton. Headed by Wharton operations, information and decisions professor Kartik Hosanagar, AIB is a research initiative that focuses on helping students expand their knowledge and application of machine learning and understand the business and societal implications of AI.

According to De Cremer, AI will never have a soul and it cannot replace human leadership qualities that let people be creative and have different perspectives. Leadership is required to guide the development and applications of AI in ways that best serve the needs of humans. The job of the future may well be [that of] a philosopher who understands technology, what it means to our human identity, and what it means for the kind of society we would like to see, he noted.

An edited transcript of the interview appears below.

AI for Business: A lot is being written about artificial intelligence. What inspired you to write Leadership by Algorithm? What gap among existing books about AI were you trying to fill?

David De Cremer: AI has been around for quite some time. The term was coined in 1956 and inspired a first wave of research until the mid-1970s. But since the beginning of the 21st century more direct applications became clear and changed our attitude towards the real potential of AI. This shift was especially fueled by events where AI started to engage with world champions in chess and the Chinese game Go. Most of the attention went, and still goes to, the technology itself: that the technology acts in ways that seem to be intelligent, which is also a simple definition of artificial intelligence.

It seems intelligent in ways that humans are intelligent. I am not a computer scientist; my background is in behavioral economics. But I did notice that the integration between social sciences, humanity, and artificial intelligence was not getting as much attention as it should. Artificial intelligence is meant to create value for society that is populated by humans; the end users always must be humans. That means AI must act, think, read, and produce outcomes in a social context.

AI is particularly good at repetitive, routine tasks and thinking systematically and consistently. This already implies that the tasks and the jobs that are most likely to be taken over by AI are the hard skills, and not so much the soft skills. In a way, this observation corresponds with what is called Moravecs paradox: What is easy for humans is difficult for AI, and what is difficult for humans seems rather easy for AI.

An important conclusion is then also that in the future developments of humans, training our soft skills will become even more important and not less as many may assume. I wanted to explain that because there are many signs today especially so since COVID-19 that we need and are required to adapt more to the new technologies. As such, that puts the use and influence of AI in our society in a dominant position. As we are becoming more aware, we are moving into a society where people are being told by algorithms what their taste is, and, without questioning it too much, most people comply easily. Given these circumstances, it does not seem to be a wild fantasy anymore that AI may be able to take a leadership position, which is why I wanted to write the book.

We are moving into a society where people are being told by algorithms what their taste is, and, without questioning it too much, most people comply easily.

AIB: Is it possible to develop AI in a way that makes technology more efficient without undermining humanity? Why does this risk exist? Can it be mitigated?

De Cremer: I believe it is possible. This relates to the topic of the book as well. [It is important] that we have the right kind of leadership. The book is not only about whether AI will replace leaders; I also point out that humans have certain unique qualities that technology will never have. It is difficult to put a soul into a machine. If we could do that, we would also understand the secrets of life. I am not too optimistic that it will [become reality] in the next few decades, but we have an enormous responsibility. We are developing AI or a machine that can do things we would never have imagined years ago.

At the same time, because of our unique qualities of having and taking perspective, proactive thinking, and being able to take things into abstraction, it is up to us how we are going to use it. If you look at the leadership today, I do not see much consensus in the world. We are not paying enough attention to training our leaders our business leaders, our political leaders, and our societal leaders. We need good leadership education. Training starts with our children. [It is about] how we train them to appreciate creativity, the ability to work together with others, take perspectives from each other, and learn a certain kind of responsibility that makes our society. So yes, we can use machines for good if we are clear about what our human identity is and the value we want to create for a humane society.

AIB: Algorithms are becoming an important part of how work is managed. What are the implications?

De Cremer: An algorithm is a model that makes data intelligent, meaning it helps us to recognize the trends that are happening in the world around us, and that are captured by means of our data collections. When analyzed well, data can tell us how to deal with our environment in a better and more efficient manner. This is what Im trying to do in the business school, by seeing how we can make our business leaders more tech savvy in understanding how, where, and why to use algorithms, automation, to have more efficient decision-making.

Many business leaders have problems making business cases for why they should use AI. They are struggling to make sense of what AI can bring to their companies. Today most of them are influenced by surveys showing that as a business you have to engage in AI adoption because everyone else is doing it. But how it can benefit your own unique company is often less well understood.

Every company has data that is unique to it. You must work with that in terms of [shaping] your strategy, and in terms of the value that your company can and wishes to create. For this to be achieved, you also have to understand the values that define your company and that make it different your competitors. We are not doing a good job training our business leaders to think like this. Rather than making them think that they should become coders themselves, they should focus on becoming a bit more tech savvy so they can pursue their business strategy in line with their values in an environment where technology is part of the business process.

This implies that our business leaders do understand what an algorithm exactly does, but also what its limits are, what the potential is, and especially so where in the decision-making chain of the company AI can be used to promote productivity and efficiency. To achieve this, we need leaders who are tech savvy enough to optimize their extensive knowledge on business processes to maximize efficiency for the company and for society. It is there that I see a weakness for many business leaders today.

Without a doubt, AI will become the new co-worker. It will be important for us to decide where in the loop of the business process do you automate, where is it possible to take humans out of the loop, and where do you definitely keep humans in the loop to make sure that automation and the use of AI doesnt lead to a work culture where people feel that they are being supervised by a machine, or being treated like robots. We must be sensitive to these questions. Leaders build cultures, and in doing this they communicate and represent the values and norms the company uses to decide how work needs to be done to create business value.

AIB: Are algorithms replacing the human mind as machines replaced the body? Or are algorithms and machines amplifying the capabilities of the mind and body? Should humans worry that AI will render the mental abilities of humans obsolete or simply change them?

De Cremer: That is one of the big philosophical questions. We can refer to Descartes here, [who discovered the] body and mind [problem]. With the Industrial Revolution, we can say that the body was replaced by machine. Some people do believe that with artificial intelligence the mind will now be replaced. So, body and mind are basically taken over by machines.

We can use machines for good if we are clear about what our human identity is and the value we want to create for a humane society.

As I outlined in my book, there is more sophistication to that. We also know that the body and mind are connected. What connects them is the soul. And that soul is not a machine. The machine at this moment has no real grasp of what it means to understand its environment or how meaning can be inferred from it. Even more important in light of the idea of humanity and AI, a machine does not think about humans, or what it means to be a human. It does not care about humans. If you die today, AI does not worry about that.

So, AI does not have a connection to reality in terms of understanding semantics and deeply felt emotions. AI has no soul. That is essential for body and mind to function. We say that one plus one is three if you want to make a great team. But in this case if we say AI or machines replace the body and then replace the mind, we still have one plus one is two, but we do not have three, we dont have the magic. Because of that, I do not believe AI is replacing our mind.

Secondly, the simple definition that I postulated earlier is that artificial intelligence represents behaviors, or decisions that are being made by a machine that seem intelligent. That definition is based on the idea that machine intelligence is able to imitate the intelligent behavior that humans show. But, that machines seem able to act in ways like humans does not mean that we are talking about the same kind of intelligence and existence.

When we look at machine learning, it is modeled after neural networks. But we also know, for example, that neuroscience still knows little, maybe not even 10%, of how the brain works. So, we cannot say that we know everything and put that in a machine and argue that it replicated the human mind completely.

The simplest example I always use is that a computer works in ones and zeroes, but people do not work in ones and zeroes. When we talk about ethics with humans, things are mostly never black or white, but rather gray. As humans we are able to make sense of that gray area, because we have developed an intuition, a moral compass in the way we grew up and were educated. As a result, we can make sense of ambiguity. Computers at the moment cannot do that. Interestingly, efforts are being made today to see whether we can train machines like we educate children. If that succeeds, then machines will come closer to dealing with ambiguity as we do.

AIB: What implications do these questions have for leadership? What role can leaders play in encouraging the design of better technology that is used in wiser rather than smarter ways?

That machines seem able to act in ways like humans does not mean that we are talking about the same kind of intelligence and existence.

De Cremer: I make a distinction between managers and leaders. When we talk about running an organization, you need both management and leadership. Management provides the foundation for companies to work in a stable and orderly manner. We have procedures so we can make things a little bit more predictable. Since the early 20th century, as companies grew in size, you had to manage companies and [avoid] chaos. Management is thus the opposite of chaos. It is about structuring and [bringing] order to chaos by employing metrics to assess goals and KPIs are achieved in more or less predictable ways. In a way, management as we know it, is a status-quo maintaining system.

Leadership, however, is not focused on the status quo but rather deals with change and the responsibility to give direction to deal with the chaos that comes along with change. That is why it is important for leadership to be able to adapt, to be agile, because once things change, as a leader you are looked upon to [provide solutions]. That is where our abilities to be creative, to think in proactive ways, understand what value people want to see and to adapt to ensure that this kind of value is achieved when change sets in.

AI will be extremely applicable to management because management is consistent, it tries to focus on the status quo, and because of its repetitiveness it is in essence a pretty predictable activity and this is basically also how an algorithm works. AI is already doing this kind of work by predicting the behavior of employees, whether they will leave the company, or whether they are still motivated to do their job. Many managerial decisions are where I see algorithms can play a big role. It starts as AI being an advisor, providing information, but then slowly moving into management jobs. I call this management by algorithm MBA. Theoretically and from a practical point of view, this will happen, because AI as we know it today in organizations is good at working with stationary data sets. It, however, has a problem dealing with complexities. This is where AI, as we know it today, falls short on the leadership front.

Computer scientists working in robotics and with self-driving cars say the biggest challenge for robots is interacting with people, physical contact, and coordinating their movements with the execution of tasks. Basically, it is more difficult for robots to work within the context of teams than sending a robot to Mars. The reason for this is that the more complex the environment, the more likely it is that robots will make mistakes. As we are less tolerant to having robots inflict harm on humans, it thus becomes a dangerous activity to have autonomous robotsand vehicles interacting with humans.

Leadership is about dealing with change. It is about making decisions that you know are valuable to humans. You need to understand what it means to be a human, that you can have human concerns, taking into account that you can be compassionate, and you can be humane. At the same time, you need to be able to imagine and be proactive, because your strategy in a changing situation may need to be adjusted to create the same value. You need to be able to make abstraction of this, and AI is not able to do this.

AIB: I am glad you brought up the question of compassion. Do you believe that algorithm-based leadership is capable of empathy, compassion, curiosity, or creativity?

[Artificial intelligence] has a problem dealing with complexities. This is where AI, as we know it today, falls short on the leadership front.

De Cremer: Startups and scientists are working on what we call affective AI. Can AI detect and feel emotions? Conceptually it is easy to understand. So, yes, AI will be able to detect emotions, as long as we have enough training data available. Of course, emotions are complex also to humans so, really understanding what emotions signify to the human experience, thats something AI will not be able to do (at least in decades to come). As I said before, AI does not understand what it means to be human, so, taking the emotional intelligence perspective of what makes us human is clearly a limit for machines. That is also why we call it artificial intelligence. It is important to point out that we can also say that humans have an AI; I call that authentic intelligence.

At this moment AI does not have authentic intelligence. People believe that AI systems cannot have authentic emotions and an authentic sense of morality. It is impossible because they do not have the empathic and existential qualities people are equipped with. Also, I am not too sure that algorithms achieve authentic intelligence easily given the fact that they do not have a soul. So, if we cannot infuse them with a common sense that corresponds to the common sense of humans, which can make sense of gray zones and ambiguity, I dont think they can develop a real sense of empathy, which is authentic and genuine.

What they can learn and that is because of the imitation principle is what we call surface-level emotions. They will be able to respond, they will scan your face, they will listen to the tone of your voice, and they will be able to identify categories of emotions and respond to it in ways that humans usually respond to. That is a surface-level understanding of the emotions that humans express. And I do believe that this ability will help machines to be efficient in most interactions with humans.

Why will it work? Because as humans we are very attuned to the ability of our interaction partners to respond to our emotions. So almost immediately and unconsciously, when someone pays attention to us, we reciprocate. Recognizing surface-level emotions would already do the trick. The deeper-level emotions correspond with what I call authentic intelligence, which is genuine, and an understanding of those type of emotions is what is needed to develop friendships and long-term connections. AI as we know it today is not even close to such an ability.

With respect to creativity, it is a similar story. Creativity means bringing forward a new idea, something that is new and meaningful to people. It solves a problem that is useful, and it makes sense to people. AI can play a role there, especially in identifying something new. Algorithms are much faster than humans in connecting information because they can scan, analyze, and observe trends in data so much faster than we do. So, in the first stage of creativity, yes, AI can bring things we know together to create a new combination so much faster and better than humans. But, humans will be needed to assess whether the new combination makes sense to solve problems humans want to solve. Creative ideas gain in value when they become meaningful to people and therefore human supervision as the final step in the creativity process will be needed.

One of the concerns we have today is that machines are not reducing inequality but enhancing it.

Let me illustrate this point with the following example: Experiments have been conducted where AI was given several ingredients to make pizzas, and some pizzas turned out to be attractive to humans, but other pizzas ended up being products that humans were unlikely to eat, like pineapple with marmite. Marmite is popular in the U.K. and according to the commercials, people love it or hate it, so, its a difficult ingredient. AI, however, does not think about whether humans will like such products or find them useful it just identifies new combinations. So, the human will always be needed to determine whether such ideas will at the end of the day be useful and regarded as a meaningful product.

AIB: What are the limits to management by algorithm?

De Cremer: When we look at it from the narrow point of view of management, there are no limits. I believe that AI will be able to do almost any managerial task in the future. That is because of the way we define management as being focused on the idea of creating stability, order, consistency, predictability, by means of using metrics (e.g., KPIs).

AIB: How can we move towards a future where algorithms may not lead but still be at the service of humanity?

De Cremer: First, all managers and leaders will have to understand what AI is. They must understand AIs potential and its limits where humans must jump in and take responsibility. Humanity is important. We have to make sure that people not only look at technology from a utility perspective, where it can make a company run more efficiently because it reduces cost by not having to hire too many employees or not training people anymore to do certain tasks.

I would like to see a society where people become much more reflective. The job of the future may well be [that of] a philosopherone who understands technology, what it means to our human identity, and what it means to the kind of society we would like to see. AI also makes us think about who we are as a species. What do we really want to achieve? Once we make AI a coworker, once we make AI a kind of citizen of our societies, I am sure the awareness of the idea Us versus them will become directive in the debates and discussions of the kind of institutes, organizations and society we would like to see. I called this awareness the new diversity in my book. Humans versus non-humans, or machines: It makes us think also about who we are, and we need that to determine what kind of value we want to create. That value will determine how we are going to use our technology.

One of the concerns we have today is that machines are not reducing inequality but enhancing it. For example, we all know that AI, in order to learn, needs data. But is data widely available to everyone or only a select few? Well, if we look at the usual suspects Amazon, Facebook, Apple and so forth we see that they own most of the data. They applied a business model where the customer became the product itself. Our data are valuable to them. As a result, these companies can run more sophisticated experiments, which are needed to improve our AI which means that technology is also in the hands of a few. Democracy of data does not exist today. Given the fact that one important future direction in AI research is to make AI more powerful in terms of processing and predicting, obviously a certain fear exists that if we do not manage AI well, and we dont think about it in terms of [whether] it is good for society as a whole, we may run into risks. Our future must be one where everyone can be tech-savvy but not one that eliminates our concerns and reflections on human identity. That is the kind of education I would like to see.

View original post here:
Artificial Intelligence Will Change How We Think About Leadership - Knowledge@Wharton

Artificial intelligence is continuously evolving and propagating across every industry. With much of the groundbreaking innovations moving the industry forward, the technology is continuously making headlines every day. AI refers to software or systems that perform intelligent tasks like those of human brains such as learning, reasoning, and judgment. Its applications range from automation and translation systems for natural languages that people use daily, to image recognition systems that help identify faces and letters from images. Today, AI is used in different forms include digital assistants, chatbots and machine learning, among others.

Heres a look at the top 10 AI Research Labs in the world that are leading the research and development in AI and related technologies.

The Alan Turing Institute is the national institute for data science and artificial intelligence headquartered in the British Library, London. The institute was created as the national institute for data science in 2015. And in 2017, as a result of a government recommendation, it added AI to its remit. Comprised of 13 universities and the UK Engineering and Physical Science Research Council, the institute helps make the UK the best place in the world for data science and AI research, collaboration, and business. Recently, the Alan Turning Institute shifted its focus to exploring the complicated ethics in the use of AI algorithms and data analytics for predictive purposes by police forces.

LIVIA is a research group accredited by TS which brings together several professors, associate members and graduate students. The laboratorys scientific orientation revolves around the key foundations of large-scale processing, analysis and interpretation of images and videos. LIVIAs R&D activities are based on six main conceptual axes and their main fields of application: (1) machine learning, (2) computer vision, (3) pattern recognition, (4) adaptive and intelligent systems, (5) information fusion, and (6) optimization of complex systems.

J.P. Morgans AI Research team is based in New York and present in key hubs worldwide. The goal of its AI Research program is to explore and advance cutting-edge research in the fields of AI and Machine Learning, also in related fields like Cryptography, to develop solutions that are most impactful to the firms clients and businesses. The firms AI Research team involves experts in various fields of AI. They pursue primary research in areas relative to its research pillars as well as concrete problems related to financial services.

The Machine Learning Research Group at the University of Oxford comprises like-minded research groupings led by local faculty. It is a sub-group within Information Engineering in the Department of Engineering Science of Oxford University. This is one of the core groupings that make up the wider community of Oxford Machine Learning and have a particularly strong overlap with the Oxford-Man Institute of Quantitative Finance. The Oxford ML Research Group uses statistics to handle both information and uncertainty in a variety of research fields, including citizen science, biology, public health, autonomous intelligent systems, and animal husbandry.

ElkanIO Research Labs is an AI research lab based in Cochin, Kerala, India. Founded in 2017 with a mindset to tackle real-world problems, this research lab has hands-on experience in developing Artificial Intelligence and Advanced Analytics solutions to cater to various industry needs. Its three major service lines include Artificial Intelligence Chatbot development, Advanced Data Analytics solutions, and Business Automation solutions powered by Computer Vision, Machine Learning, Deep Learning, and NLP solutions.

As a research institute at the Massachusetts Institute of Technology (MIT), CSAIL is the largest on-campus Laboratory for Computer Science and AI. CSAILs research activities are organized around a number of semi-autonomous research groups, each of which is headed by one or more professors or research scientists. These groups are divided up into seven general areas of research: AI, Computational biology, Graphics and vision, Language and learning, Theory of computation, Robotics, and Systems, including computer architecture, databases, distributed systems, networks and networked systems, and software engineering among others.

UTCS AI-Lab addresses the central challenges of machine cognition, both from a theoretical perspective and from an empirical, implementation-oriented perspective. The Lab has expanded to seven faculty in core areas of AI, with about 50 Ph.D. students, numerous research staff, and a dozen affiliated faculty in related departments. It continues to investigate the challenges of machine cognition, especially machine learning, knowledge representation and reasoning, and robotics.

Microsoft Research AI pursues the use of machine intelligence in new ways to empower people and organizations, including systems that deliver new experiences and capabilities that help people be more efficient, engaged and productive. It brings together the range of talent across Microsoft Research to deliver ground-breaking advances in AI. This R&D initiative coalesces advances in machine learning with innovations in language and dialog, human-computer interaction, and computer vision to solve some of the challenges in AI.

The AI Research Lab brings together UC Berkeley researchers across the areas of computer vision, machine learning, NLP, planning, control, and robotics. The Lab includes over 50 faculty and more than 300 graduate students and postdoctoral researchers pursuing research on fundamental advances in the above areas as well as cross-cutting themes. Those include multi-modal deep learning, human-compatible AI, and connecting AI with other scientific disciplines and the humanities.

USC Information Sciences Institute is a world leader in research and development of advanced information processing, computer and communications technologies. A unit of the University of Southern Californias Viterbi School of Engineering, ISI is one of the nations largest, most successful university-affiliated computer research institutes. The institutions work ranges from theoretical basic research, such as core engineering and computer science discovery, to applied R&D, such as design and modeling of innovative prototypes, and devices.

Read the original:
Top 10 Artificial Intelligence Research Labs in the World - Analytics Insight

Healthcare executives today believe AI will deliver value for the industry faster than previously thought, according to a new survey of senior healthcare executives representing leading hospitals, health plans, life sciences organizations and employers.

The third annual Optum Survey on AI in Health Care found that 59% of respondents expect their organizations to see a full return on their AI investments in under three years. Thats up 90% since 2018, when only 31% of respondents expected to break even that quickly.

The overall anticipated time frame to achieve ROI was 3.6 years in this years survey, down from 5.3 years in 2018 and 4.7 years in 2019.

Confidence in recognizing cost savings from AI appeared to increase as organizations progressed on the maturity curve. Among those who identified themselves as being in the late stages of AI deployment, 57% indicated theyd achieve their ROI in less than two years, as compared to respondents in the early (33%) and mid (26%) stages.

The dramatic drop in the amount of time it will take to achieve ROI is underscored by the effects of a turbulent 2020. In fact, 47% of the leaders reported that the effects of the pandemic would delay their achievement of ROI a finding that suggests the tailwinds of the underlying trend are much stronger than the headwinds of COVID-19 and its economic and social consequences.

AI in the current COVID-19 climate

The survey reported that the executives affirm the strategic importance of AI initiatives in healthcare and that they broadly embrace AI across the industry. Nearly all healthcare leaders98% of themhave an AI strategy in place or plan to create one. That includes the 44% whose organizations have already implemented theirs.

In the survey, healthcare execs prioritized three applications they planned to tap AI for, and each has immediate implications in the battle against the pandemic and its economic and social consequences as we navigate forward:

Monitor data from the Internet of Things devices, such as wearable technology (40%). Internet-connected remote patient monitoring devices like these enable more complete virtual health offerings. AI can also help identify signals and trends within those data streams.

Accelerate research for new therapeutic or clinical discoveries (37%). AI can help prioritize potential investigative targets for treatments or vaccines.

Assign codes for accurate diagnosis and reimbursement (37%). This helps automate business processes to help organizations achieve more even when resources are under duress.

AI creates demands for expertise that will take it to the next level

In broadly confirming AIs strategic value, 95% of healthcare executives said hiring talent with experience developing AI is a priority, with 66% of executives in late stages of AI deployment strongly agreeing, compared to 42% in early and 31% in middle stages.

In addition to building AI competency itself, the ability to act upon AI-driven recommendations is critical. To that end, 92% of the surveyed executives expect that their staff who receive AI-driven insights will understand how the AI works. That finding signals the widespread need for knowledge about analytics, predictions and data streams outside of an organizations traditional information technology or informatics teams.

Executives were split on hiring advanced analytic talent to build the AI (51%) versus business talent to apply the AI-driven results (49%).

Learn more about what healthcare execs are saying about AIs power to:

More:
2020 AI survey: Confidence in artificial intelligence expands as health industry leaders project faster return on investment - Healthcare IT News

There are a plethora of AI initiatives in progress across thehealthcareindustry. From drug discovery to thermal scans, AI has transformed this sector significantly over the past decade. While we know AIs contributions to physical healthcare, AI is also easing mental health concerns too. According to the Substance Abuse and Mental Health Service Administration (SAMHSA)s 2016 reporton drug use and health, only 63% of adults identified as having had at least onemajor depressive episodereported receiving any kind of treatment.

In the US alone, one in five adultssuffersfrom a form of mental illness. Suicide rates are at an all-time high, 115 peopledie daily from substance abuse, and one in eight Americans over 12 years oldtake an antidepressantevery day. The economic burden of depression alone isestimatedto be a minimum of US$210 billion annually, which includes costs due to increased absenteeism and reduced productivity in the workplace. The situation is grim in other regions as well. For instance, in Europe, 83 million people are struggling with mental health. Other than this, people also have to endure stigma, lack of mental health professionals and high costs of counselling sessions too.

Currently, during the world pandemic crisis, even the COVID-19 has been tangibly affecting millions of people in terms of their mental peace. Rates of depression and panic attacks are much higher than normal.Isolation due to social distancing has triggered sleep deprivation, social anxiety, and reduced happiness while also causing people to worry about their jobs. As per a report, 70% of people have had more stress and anxiety at work this year than any other previous year. This increased stress and anxiety have negatively impacted the mental health of 78% of the global workforce, causing more stress (38%), a lack of work-life balance (35%), burnout (25%), depression due to lack of socialization (25%), and loneliness (14%). A recent report by Deloittesuggests COVID-19s impact on mental health could last for years

While, AI is proving to be an effective way for clinicians to both make the best of the time they do have with patients, and bridge any gaps in access, it has also helped in early prediction and diagnosis of the diseases too. So, incorporating AI to address mental health issues can achieve similar promising results. For starters, this can be done by employing AI into digital interventions, like web andsmartphoneapps, to enhance user experience and optimize personalized mental health care. One can analyze modern streams of abundant data as a means to develop prediction or detection models for mental health conditions. E.g. Quartet Health, has reduced hospitalization of patients by 15-25% by screening patient medical histories and behavioral patterns to uncover undiagnosed mental health problems.

Vanderbilt University Medical Center in Nashville has created a machine learning algorithm that uses hospital admissions data, including age, gender, zip code, medication, and diagnostic history, to predict the likelihood of any given individual taking their own life.Scientists are experimenting with linear classifiers of Natural Language Processing (NLP) to risk assessment in possible PTSD cases.

Also, while AI would not replace existing therapists and psychiatrists, it surely provides a medium where people can talk about their struggles without the fear of being judges or facing stigma. Further, AIcan help doctors and therapists increase emotional awareness for their patients, such as in expressing empathy. Research has also proved that AI helped employees improvetheir mental health at work.

Next, we have emotional AI-powered chatbots (like Wysa, Woebot) that can provide unprecedented accessibility by being available 24/7 at little to no cost. These apps collect data that allow them to create a level of therapeutic rapport with users and offer relevant responses. Mood tracking apps like Woebot, which is created by a team of Stanford psychologists and AI experts, uses brief daily chat conversations, mood tracking, curated videos, and word games to help people manage mental health. This is faster and comfortable than traditional practice in mental health where professionals rely on the individual to observe and self-report indicative changes. E.g. IBMs Computational Psychiatry and Neuroimaging group, alongside several universities, have built a model using NLP to predict the onset of psychosis in patients. This model can detect differences in speech patterns between high-risk patients who develop psychosis and those who did not. At present, a team of scientists at Dublin, Ireland-based startup Behavidence is currently preparing to launch an effective digital phenotyping solution that can provide an accurate psychiatric diagnosis for ADHD.

Mental health will likely remain a major challenge in todays world due to various reasons. Although AI for mental health still needs to deal with many complexities, its applications and tools are doing an appreciable job in alleviating this issue for many. Soon it will be well equipped to mitigate and manage the stress, depression and trauma, things which are living hell for many.

See original here:
Easing the lives of Mental Health Patients via Artificial Intelligence - Analytics Insight