Monthly Archives: April 2022

OAKLAND, Calif., April 6 (Reuters) - Microsoft Corp's (MSFT.O) LinkedIn boosted subscription revenue by 8% after arming its sales team with artificial intelligence software that not only predicts clients at risk of canceling, but also explains how it arrived at its conclusion.

The system, introduced last July and described in a LinkedIn blog post on Wednesday, marks a breakthrough in getting AI to "show its work" in a helpful way.

While AI scientists have no problem designing systems that make accurate predictions on all sorts of business outcomes, they are discovering that to make those tools more effective for human operators, the AI may need to explain itself through another algorithm.

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The emerging field of Explainable AI, or XAI, has spurred big investment in Silicon Valley as startups and cloud giants compete to make opaque software more understandable and has stoked discussion in Washington and Brussels where regulators want to ensure automated decision-making is done fairly and transparently.

AI technology can perpetuate societal biases like those around race, gender and culture. read more Some AI scientists view explanations as a crucial part of mitigating those problematic outcomes.

U.S. consumer protection regulators including the Federal Trade Commission have warned over the last two years that AI that is not explainable could be investigated. The EU next year could pass the Artificial Intelligence Act, a set of comprehensive requirements including that users be able to interpret automated predictions.

Proponents of explainable AI say it has helped increase the effectiveness of AIs application in fields such as healthcare and sales. Google Cloud (GOOGL.O) sells explainable AI services that, for instance, tell clients trying to sharpen their systems which pixels and soon which training examples mattered most in predicting the subject of a photo.

But critics say the explanations of why AI predicted what it did are too unreliable because the AI technology to interpret the machines is not good enough.

LinkedIn and others developing explainable AI acknowledge that each step in the process - analyzing predictions, generating explanations, confirming their accuracy and making them actionable for users - still has room for improvement.

But after two years of trial and error in a relatively low-stakes application, LinkedIn says its technology has yielded practical value. Its proof is the 8% increase in renewal bookings during the current fiscal year above normally expected growth. LinkedIn declined to specify the benefit in dollars, but described it as sizeable.

Before, LinkedIn salespeople relied on their own intuition and some spotty automated alerts about clients' adoption of services.

Now, the AI quickly handles research and analysis. Dubbed CrystalCandle by LinkedIn, it calls out unnoticed trends and its reasoning helps salespeople hone their tactics to keep at-risk customers on board and pitch others on upgrades.

LinkedIn says explanation-based recommendations have expanded to more than 5,000 of its sales employees spanning recruiting, advertising, marketing and education offerings.

"It has helped experienced salespeople by arming them with specific insights to navigate conversations with prospects. Its also helped new salespeople dive in right away," said Parvez Ahammad, LinkedIn's director of machine learning and head of data science applied research.

TO EXPLAIN OR NOT TO EXPLAIN?

In 2020, LinkedIn had first provided predictions without explanations. A score with about 80% accuracy indicates the likelihood a client soon due for renewal will upgrade, hold steady or cancel.

Salespeople were not fully won over. The team selling LinkedIn's Talent Solutions recruiting and hiring software were unclear on how to adapt their strategy, especially when the odds of a client not renewing were no better than a coin toss.

Last July, they started seeing a short, auto-generated paragraph that highlights the factors influencing the score.

For instance, the AI decided a customer was likely to upgrade because it grew by 240 workers over the past year and candidates had become 146% more responsive in the last month.

In addition, an index that measures a client's overall success with LinkedIn recruiting tools surged 25% in the last three months.

Lekha Doshi, LinkedIn's vice president of global operations, said that based on the explanations sales representatives now direct clients to training, support and services that improve their experience and keep them spending.

But some AI experts question whether explanations are necessary. They could even do harm, engendering a false sense of security in AI or prompting design sacrifices that make predictions less accurate, researchers say.

Fei-Fei Li, co-director of Stanford University's Institute for Human-Centered Artificial Intelligence, said people use products such as Tylenol and Google Maps whose inner workings are not neatly understood. In such cases, rigorous testing and monitoring have dispelled most doubts about their efficacy.

Similarly, AI systems overall could be deemed fair even if individual decisions are inscrutable, said Daniel Roy, an associate professor of statistics at University of Toronto.

LinkedIn says an algorithm's integrity cannot be evaluated without understanding its thinking.

It also maintains that tools like its CrystalCandle could help AI users in other fields. Doctors could learn why AI predicts someone is more at risk of a disease, or people could be told why AI recommended they be denied a credit card.

The hope is that explanations reveal whether a system aligns with concepts and values one wants to promote, said Been Kim, an AI researcher at Google.

"I view interpretability as ultimately enabling a conversation between machines and humans," she said.

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Reporting by Paresh Dave; Editing by Kenneth Li and Lisa Shumaker

Our Standards: The Thomson Reuters Trust Principles.

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AI is explaining itself to humans. And it's paying off - Reuters

Its not new that the emerging artificial intelligence technology aims to take over the writing space.

High-end and intermediate writers have expressed cynical views and even fears over the AI writing software introduction.

For proponents of the AI writing application, its not so! According to them, the concept behind the creation is to help lessen the workload of writers.

In the meantime, the number of AIs has surpassed expectations. From small companies to big names in tech, AIs are attempting to become the next big thing for content marketing.

In fact, due to the improvement in its machine language and data analytics, some companies prefer AI content marketing.

This begs the question, is AI the future of writing? Or will it replace the human writing form?

Read on!

If youve been wondering what goes on behind every AI, its simple, a machine language.

AI writing tools usenatural language generationto produce written words from mere data. You just input data in, and the rest is history.

An AI is effective when a large amount of data needs conversion into written language that anyone can understand.

Scientists didnt stop at a mere natural language generation; more work began after the discovery in 2016.

They rebranded and created a more advanced AI that didnt need data labeling while saving time and money.

In May 2020, another model was created. Its called OpenAIs GPT-3 (Generative Pre-trained Transformer-3).

This new and advanced machine language is the largest neural network globally. The machine has a model with over 175 million parameters.

The GPT-3 is different from other AIs because it processes information like the human thinking faculty.

It executes tasks like answering questions, filling in blanks, publishing articles, writing songs, jokes, and even questions about the philosophical aspect of life!

There are even better and more advanced ALs being created. In particular, some companies have copied the language system of the OpenAIsGTP-3and made better improvements.

In May 2020, Google launched a new chatbot called LAMDA. Its designed to hold meaningful, emotional, and intellectual conversations.

Whats more, Beijing has attempted to create the first living AI. In June 2020, the Beijing Academy of Artificial Intelligence (BAAI) launched a new AI calledWu Dao 2.0.

The AI gave life to its first virtual student,Hua Zhubingto write songs and codes and possess a large memory.

This has become a lingering question in every writers mind and probably a writers worst fear, especially writers in the business ofcontent writing or copywriting.

While AI technology keeps advancing, its arguably not going to be the future of writing.

Writers are more skilled in capturing the essence and reader perception. Itll take years of research for any AI to exhibit such traits. An AI cant write emphatically as a human would.

Although AI has shown great dexterity and expertise in writing, there are still major gaps that cant be filled.

Below are a few reasons why writers need not worry about AIs for now:

An AI lacks the uniqueness human writers bring to their articles. Its an intricate factor that distinguishes the pro from the amateur.

AIs may be perfect for data gathering and analyzing complex words but possess poor creative analytics.

They poorly express themselves due to a lack of cognition and emotion. Only humans can process such complexities.

AIs produce whatever you run into them. The process is like garbage in garbage out.

The workload still falls on a human to carefully reread and edit AI-generated articles.

Yes, it might be difficult to detect an AI-written article. However, AIs struggle to compose coherent and engaging content to captivate readers. Engagement is the footstone of every good content.

Writers are more skilled in capturing the essence of every article. It may take years of research for AIs to exhibit such traits.

If theres one thing an AI greatly lacks in information presentation, its a lack of direct and multiple evaluations.

For instance, an AI cant interpret a proverb or an idiom. They arent recognizable in data analysis.

Also, they cant differentiate between the linguistic complexities, like when not to use offensive words.

For now, human writers have nothing to worry about. AIs and humans can coexist symbiotically without one dominating the other.

Though many believe its economical and more reliable than human writers. However, the barrier to the above statement is the cost of an AI to start up. Only big tech companies can afford excellent and effective AI writing tools.

The risk-on human writers are quite low. However, it shouldnt stop you from honing your skill!

Sam Altman, CEO of Open AIs, in a tweet published in early June 2021, stated that AIs might likely affect physical jobs more than remote jobs such as coding, writing, administrative jobs, and co.

Whether we like it or not, AIs are here to stay. We cant fight them. However, we can create a means to incorporate them into the physical fold without any job losses.

They immensely contribute to accelerating a writers process and simplifying the workload.

We already use low-resource AIs like Grammarly and plagiarism checkers. Still, human editors and proofreaders are thriving.

Photo by Reports Monitor from Flickr

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Is artificial intelligence the future of writing? - The Rude Baguette

In machine learning, understanding why a model makes certain decisions is often just as important as whether those decisions are correct. For instance, a machine-learning model might correctly predict that a skin lesion is cancerous, but it could have done so using an unrelated blip on a clinical photo.

While tools exist to help experts make sense of a models reasoning, often these methods only provide insights on one decision at a time, and each must be manually evaluated. Models are commonly trained using millions of data inputs, making it almost impossible for a human to evaluate enough decisions to identify patterns.

Now, researchers at MIT and IBM Research have created a method that enables a user to aggregate, sort, and rank these individual explanations to rapidly analyze a machine-learning models behavior. Their technique, called Shared Interest, incorporates quantifiable metrics that compare how well a models reasoning matches that of a human.

Shared Interest could help a user easily uncover concerning trends in a models decision-making for example, perhaps the model often becomes confused by distracting, irrelevant features, like background objects in photos. Aggregating these insights could help the user quickly and quantitatively determine whether a model is trustworthy and ready to be deployed in a real-world situation.

In developing Shared Interest, our goal is to be able to scale up this analysis process so that you could understand on a more global level what your models behavior is, says lead author Angie Boggust, a graduate student in the Visualization Group of the Computer Science and Artificial Intelligence Laboratory (CSAIL).

Boggust wrote the paper with her advisor, Arvind Satyanarayan, an assistant professor of computer science who leads the Visualization Group, as well as Benjamin Hoover and senior author Hendrik Strobelt, both of IBM Research. The paper will be presented at the Conference on Human Factors in Computing Systems.

Boggust began working on this project during a summer internship at IBM, under the mentorship of Strobelt. After returning to MIT, Boggust and Satyanarayan expanded on the project and continued the collaboration with Strobelt and Hoover, who helped deploy the case studies that show how the technique could be used in practice.

Human-AI alignment

Shared Interest leverages popular techniques that show how a machine-learning model made a specific decision, known as saliency methods. If the model is classifying images, saliency methods highlight areas of an image that are important to the model when it made its decision. These areas are visualized as a type of heatmap, called a saliency map, that is often overlaid on the original image. If the model classified the image as a dog, and the dogs head is highlighted, that means those pixels were important to the model when it decided the image contains a dog.

Shared Interest works by comparing saliency methods to ground-truth data. In an image dataset, ground-truth data are typically human-generated annotations that surround the relevant parts of each image. In the previous example, the box would surround the entire dog in the photo. When evaluating an image classification model, Shared Interest compares the model-generated saliency data and the human-generated ground-truth data for the same image to see how well they align.

The technique uses several metrics to quantify that alignment (or misalignment) and then sorts a particular decision into one of eight categories. The categories run the gamut from perfectly human-aligned (the model makes a correct prediction and the highlighted area in the saliency map is identical to the human-generated box) to completely distracted (the model makes an incorrect prediction and does not use any image features found in the human-generated box).

On one end of the spectrum, your model made the decision for the exact same reason a human did, and on the other end of the spectrum, your model and the human are making this decision for totally different reasons. By quantifying that for all the images in your dataset, you can use that quantification to sort through them, Boggust explains.

The technique works similarly with text-based data, where key words are highlighted instead of image regions.

Rapid analysis

The researchers used three case studies to show how Shared Interest could be useful to both nonexperts and machine-learning researchers.

In the first case study, they used Shared Interest to help a dermatologist determine if he should trust a machine-learning model designed to help diagnose cancer from photos of skin lesions. Shared Interest enabled the dermatologist to quickly see examples of the models correct and incorrect predictions. Ultimately, the dermatologist decided he could not trust the model because it made too many predictions based on image artifacts, rather than actual lesions.

The value here is that using Shared Interest, we are able to see these patterns emerge in our models behavior. In about half an hour, the dermatologist was able to make a confident decision of whether or not to trust the model and whether or not to deploy it, Boggust says.

In the second case study, they worked with a machine-learning researcher to show how Shared Interest can evaluate a particular saliency method by revealing previously unknown pitfalls in the model. Their technique enabled the researcher to analyze thousands of correct and incorrect decisions in a fraction of the time required by typical manual methods.

In the third case study, they used Shared Interest to dive deeper into a specific image classification example. By manipulating the ground-truth area of the image, they were able to conduct a what-if analysis to see which image features were most important for particular predictions.

The researchers were impressed by how well Shared Interest performed in these case studies, but Boggust cautions that the technique is only as good as the saliency methods it is based upon. If those techniques contain bias or are inaccurate, then Shared Interest will inherit those limitations.

In the future, the researchers want to apply Shared Interest to different types of data, particularly tabular data which is used in medical records. They also want to use Shared Interest to help improve current saliency techniques. Boggust hopes this research inspires more work that seeks to quantify machine-learning model behavior in ways that make sense to humans.

This work is funded, in part, by the MIT-IBM Watson AI Lab, the United States Air Force Research Laboratory, and the United States Air Force Artificial Intelligence Accelerator.

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Does this artificial intelligence think like a human? - MIT News

Fake faces created by artificial intelligence (AI) are considered more trustworthy than images of real people, a study has found.

The results highlight the need forsafeguards to prevent deep fakes, which have already been used for revenge porn, fraud and propaganda, the researchers behind the report say.

Real (R) and synthetic (S) faces were rated for trustworthiness with statistically significant results. (Image: PNAS)

The study by Dr Sophie Nightingale from Lancaster University in the UK and Professor Hany Farid from the University of California, Berkeley, in the US asked participants to identify a selection of 800 faces as real or fake, and to rate their trustworthiness.

After three separate experiments, the researchers found the AI-created synthetic faces were on average rated 7.7% more trustworthy than the average rating for real faces. This is statistically significant, they add. Thethree faces rated most trustworthy were fake, while the four faces rated most untrustworthy were real, according to the magazine New Scientist.

The fake faces were created usinggenerative adversarial networks (GANs), AI programmes that learn to create realistic faces through a process of trial and error.

The study,AI-synthesized faces are indistinguishable from real faces and more trustworthy, is published in the journal, Proceedings of the National Academy of Sciences of the United States of America (PNAS).

It urges safeguards to be put into place, which could include incorporating robust watermarks into the image to protect the public from deep fakes.

Guidelines on creating and distributing synthesized images should also incorporate ethical guidelines for researchers, publishers, and media distributors, the researchers say.

The four most (top row) and four least (bottom row) trustworthy faces, according to the study. (Image: PNAS)

Using AI responsibly is the immediate challenge facing the field of AI governance, the World Economic Forum says.

In its report,The AI Governance Journey: Development and Opportunities, the Forum says AI has been vital in progressing areas like innovation, environmental sustainability and the fight against COVID-19. But the technology is also challenging us with new and complex ethical issues and racing ahead of our ability to govern it.

The report looks at a range of practices, tools and systems for building and using AI.

These include labelling and certification schemes; external auditing of algorithms to reduce risk; regulating AI applications, and greater collaboration between industry, government, academia and civil society to develop AI governance frameworks.

Republished with permission of the World Economic Forum. Read theoriginal article.

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People trust AI fake faces more than real ones, study finds - Big Think

AI is improving and despite the many obstacles that it faces, researchers and scientists are still focusing on the many upgrades and factors that may better the intelligent system. OpenAI brought its new DALL-E 2 to the world and it focuses on bringing better AI to its systems, particularly with a new vision and language that aims to surpass its past ventures.

(Photo : Steve Jennings/Getty Images for TechCrunch)SAN FRANCISCO, CALIFORNIA - OCTOBER 03: OpenAI Co-Founder & CEO Sam Altman speaks onstage during TechCrunch Disrupt San Francisco 2019 at Moscone Convention Center on October 03, 2019 in San Francisco, California.

OpenAI's DALL-E 2is a new system from the company and its aim is to bring realistic images and art that will border on a natural language that the system would understand and perceive better. The new system picks up from the DALL-E's first release, and from there, the AI's capabilities are seeing an improvement in its offers.

Text is enough for the DALL-E 2 to create realistic images and art from scratch. OpenAI improved its systems for better use of AI, and the results bring impeccable results that may only be seen on science fiction shows that create these types of futuristic AIs for the public.

The new AI brings a focus on the system that aims to help the many creators now.

Read Also:Drug-Developing AI Identifies 40,000 Bioweapon Chemicals For Just 6 Hours, But Here's a Warning

According toTech Crunch, some of the keys to improving AI is to bring a significant change to vision and language, particularly with the way it perceives things and creates output from them. The research and many changes to the code for deep learning are a massive move for science, especially now that the world is heavily reliant on AI.

Many companies use AI now, and most of the world's systems are relying on self-learning technology that focuses on deep knowledge of the many systems in the world. A recent venture by NVIDIA focused onbringing 3D images from 2D renders it has, with the use of artificial intelligence to help its cause.

There are many speculations and doubts against AI, especially with the skills that it can do that rival the sentient beings from the many processes and outputs in life. One example would be thecapabilities of AI to create art on its own, and the system focuses on an original artwork that it conceptualized without the need for human intervention.

Artificial intelligence is a vast study now, and it focuses on the many features that aim to help humans in their daily lives and the processes that humans face every day. OpenAI's efforts in the AI industry are a massive help to many, bringing their capabilities to the table, aiming to bring many offers to everyone in their daily lives.

Related Article:Cardiac Arrest-Detecting AI Now Under Development; Here's How It Reduces Death Cases

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AI to Improve with Vision and LanguageNew OpenAIs DALL-E 2 is One Promising Technology - Tech Times

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One of the main roadblocks preventing the enterprise from putting artificial intelligence (AI) into action is the transition from development and training to production environments. To gain real benefits from the technology, this must be done at the speed and scale of todays business environment, which few organizations are capable of doing.

This is why the interest in merging AI with devops is gaining steam. Forward-leaning enterprises are trying to blend machine learning (ML) in particular with the traditional devops model, which creates an MLops process that streamlines and automates the way intelligent applications are developed and deployed and then updated on a continual basis to increase the value of its operations over time.

According to data scientist Aymane Hachcham, MLops helps the enterprise deal with a number of significant issues when it comes to effectively building and managing intelligent applications. For one thing, the data sets used in the training phase are extremely large and are continuously expanding and changing. This requires constant monitoring, experimentation, adjustment and retraining of AI models, all of which becomes time-consuming and expensive under traditional, manually driven development and production models.

To effectively implement MLops, the enterprise will need to develop a number of core capabilities, such as full lifecycle tracking, metadata optimized for model training, hyperparameter logging and a solid AI infrastructure consisting not only of server, storage and networking solutions but software tools capable of rapid iteration of new machine learning models. And all of this will have to be designed around the two main forms of MLops: predictive, which attempts to chart future outcomes based on past data and prescriptive, which strives to make recommendations before decisions are made.

Mastering this discipline is the only plausible way for AI to trickle down from the Fortune 500 enterprise to the rest of the world, says Greenfield Partners Shay Grinfeld and Itay Inbar. The fact is, upwards of 90 % of ML projects fail under current development and deployment frameworks, which is simply not tenable for the vast majority of organizations. MLops provides a dramatically more efficient development pipeline that not only reduces the overall cost of the process but can turn failures into successes at a rapid pace. The end result is that the barriers to AI implementation drop to a level that is comfortable for the vast majority of enterprises, leading to widespread distribution and eventual integration into mainstream data operations.

MLops is still an emerging field, so it may be tempting to write it off as just another techy buzzword, says business analytics and data science consultant Sibanjan Das. But its track-record so far has been pretty good, provided it is designed the right way and targeted at the proper goal: to maximize model performance and improve ROI. This requires careful coordination between the various components that create an MLops environment, such as the CI/CD pipeline itself, as well as model serving, version control and data monitoring. And dont forget to build robust security and governance mechanisms to minimize the risk of the ML models activities and the chance of it being compromised.

Even though MLops is designed for automation and even autonomy, dont overlook the human element as a key driver of successful outcomes. A recent report by Dataiku noted that over the past year, companies have come to the realization that they cannot scale AI without building diverse teams that can implement and benefit from the technology. MLops should be a critical component of this strategy because it supports diversification in the development, deployment and management of AI projects. And just judging by Gartners MLops framework, a broad set of skills will be required to ensure that outcomes provide top value to the enterprise business model.

Even the most advanced technology is of little value if it cannot successfully transition from the lab to the real world. AI is now at the point where it must begin making a valuable contribution to humanity or it will become the digital equivalent of the Edsel: flashy and full of gadgets but with little practical value.

MLops cannot guarantee success, of course, but it can lower the cost of experimentation and failure, while at the same time putting it in the hands of more people who can figure out for themselves how to use it.

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MLops: The Key to Pushing AI into the Mainstream - VentureBeat

ByLisa Ercolano

Johns Hopkins University and Amazon are teaming up to harness the power of artificial intelligence to transform the way humans interact online and with the world. The new JHU + Amazon Initiative for Interactive AI, housed in the Johns Hopkins Whiting School of Engineering, will leverage the university's world-class expertise in interactive AI to advance groundbreaking technologies in machine learning, computer vision, natural language understanding, and speech processing; democratize access to the benefits of AI innovations; and broaden participation in research from diverse, interdisciplinary scholars and other innovators.

Amazon's investment will span five years, comprising doctoral fellowships, sponsored research funding, gift funding, and community projects. Sanjeev Khudanpur, an associate professor of electrical and computer engineering at the Whiting School, will serve as the initiative's founding director. Khudanpur is an expert in the application of information-theoretic methods to human language technologies such as automatic speech recognition, machine translation, and natural language processing.

"Hopkins is already renowned for its pioneering work in these areas of AI, and working with Amazon researchers will accelerate the timetable for the next big strides," Khudanpur said. "I often compare humans and AI to Luke Skywalker and R2D2 in Star Wars: They're able to accomplish amazing feats in a tiny X-wing fighter because they interact effectively to align their complementary strengths. I am very excited at the prospect of the Hopkins AI community coming together under the auspices of this initiative, and charting the future of transformational, interactive AI together with Amazon researchers,"

Ed Schlesinger, dean of the Whiting School, said, "We are very excited to work with Amazon in this new initiative. We value the challenges that they bring us and the life-changing potential of the solutions we will create together, and look forward to strengthening our work together over the coming years."

Amazon's funding will support a broad range of activities, including annual fellowships for doctoral students; research projects led by Hopkins Engineering faculty in collaboration with postdoctoral researchers, undergraduate and graduate students, and research staff; and events and activities, such as lectures, workshops, and competitions aimed at making AI activities more accessible to the general public in the Baltimore-Washington region.

Prem Natarajan, Alexa AI vice president of natural understanding, says the partnership underscores Amazon's commitment to addressing the greatest challenges in Al, democratizing access to the benefits of Al innovations, and broadening participation in research from diverse, interdisciplinary scholars and other innovators.

"This initiative brings together the top talent at Amazon and Johns Hopkins in a joint mission to drive groundbreaking advances in interactive and multimodal AI," Natarajan said. "These advances will power the next generation of interactive AI experiences across a wide variety of domainsfrom home productivity to entertainment to health."

The two organizations have teamed up in the past, with four Johns Hopkins faculty members joining Amazon as part of its Scholars program: Ozge Sahin, a professor of operations management and business analytics at the Johns Hopkins Carey Business School, in 2019, and in 2020, Gregory Hager, Mandell Bellmore Professor of Computer Science; Ren Vidal, Herschel Seder Professor of Biomedical Engineering and director of the Mathematical Institute for Data Science; and Marin Kobilarov, associate professor of mechanical engineering.

The new initiative will build on Hopkins Engineering's existing strengths in the areas of machine learning, computer vision, natural language understanding, and speech processing. Its Mathematical Institute for Data Science conducts cutting-edge research on the mathematical, statistical, and computational foundations of machine learning and computer vision. The Center for Imaging Science and the Laboratory for Computational Sensing and Robotics conduct fundamental and applied research in nearly every area of basic and applied computer vision. The university's Center for Language and Speech Processing, one of the largest and most influential academic research centers of its kind in the world, conducts research in acoustic processing, automatic speech recognition, cognitive modeling, computational linguistics, information extraction, machine translation, and text analysis. CLSP researchers conducted some of the foundational research that led to the development of digital voice assistants.

"AI has tremendous potential to enhance human abilities, and to reach it, AI of the future will interact with humans the same way we naturally interact with each other. What endeared Amazon Alexa to users was the effortlessness of the interaction. I envision that the research done under this initiative will make it possible for us to use much more powerful AI in equally effortless ways, regardless of our own physical limitations," Khudanpur said.

Hager, a director for Amazon Physical Retail, and Vidal, currently an Amazon Scholar in visual search and AR, were instrumental in helping Amazon and JHU establish the collaboration.

"Computer vision and machine learning are transforming the way in which humans shop, share content, and interact with each other," Vidal said. "This partnership will lead to new collaborations between JHU and Amazon scientists that will help translate cutting-edge advances in deep learning and visual recognition into algorithms that help humans interact with the world."

Seth Zonies, a director of business development for Johns Hopkins Technology Ventures, the university's commercialization and industry collaboration arm, said, "This collaboration represents the opportunity to harness academic ingenuity to address needs in society through industry collaboration. The engineering faculty at Johns Hopkins are committed to applied research, and Amazon is at the forefront of product development in this field. We expect this collaboration to result in deployable, high-impact innovation."

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Johns Hopkins and Amazon collaborate to explore transformative power of AI - The Hub at Johns Hopkins

AI has extensively transformed the modern enterprise and tech ecosystem in several ways

Over the past few years, artificial intelligence has become a new reality for enterprises and business leaders across global industries. With the integration of robotics and IoT devices, machines are being made to think and perform at an entirely different level, which can possibly enable them to outsmart humans in the future. These machines have been able to learn, adapt, and perform with unprecedented agility. Even though artificial intelligence has its own perks and drawbacks, its rapid adoption by global businesses has proved its worth as the holy grail of the modern tech industry.

If we explore the technology on a smaller scale, artificial intelligence has made its way into peoples lives in one way or the other, even if individuals are not quite aware of its existence in their lives. Starting from voice assistants on our mobile phones to automotive tools that we use on a daily basis, artificial intelligence has encouraged the personalization of services to enhance customer experiences to a great extent. Currently, its importance embarks on making our lives easier, reducing human efforts as much as possible, and working in an automated fashion.

Within the past couple of years, especially owing to the pandemic, AI adoption and innovation have skyrocketed as business leaders became quite confident in AIs potential to deal with the toughest global challenges and yield results that will save humanity from the ongoing crises. The technology has been especially beneficial to healthcare business leaders and practicing professionals who overwhelmingly believed that AI can mitigate their current complexities in finding solutions to the most difficult challenges.

Now, if we analyze the role of AI on a global basis, we will observe that the technology has become the reason behind the development and integration of several avant-garde technologies like the metaverse, Web 3.0, and such others for industries. In fact, tech giants believe that AI will be the primary technology that will enhance the functionalities of emerging and trending technologies like virtual reality, the metaverse, and the 5G, ones that are supposed to converge together to provide a possibly seamless internet experience to individuals and become one of the leading iterations of the internet.

AI is also responsible for the evolution of supercomputers into superfast computers that can be used to manage and interpret vast quantities of data, within a few minutes. The technology has ensured a global race, involving tech giants who have participated in it to build the fastest supercomputers in the world. Increasing adoption of cloud computing and other cloud technologies is one of the primary reasons that has acted as a catalyst, fueling this growth.

These advancements are accompanied by distinct drawbacks that hinder AI to unlock its true potential. Nevertheless, the industry is booming with remarkable achievements, enabling public interest, as companies all over the world are acting towards outperforming each other to yield the best possible solutions to the most complex demands.

To conclude, we can evidently say that the advent of artificial intelligence in our professional and personal lives has been a blessing in disguise. Companies, big or small, are striving toward improving processes and enabling AI to become the core technology responsible for the evolution of the modern tech industry. Whether you are consciously using AI or not is hard to interpret, but it is quite clear that it is impossible to ignore AIs role and contribution to the modern tech revolution.

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Analytics Insight is an influential platform dedicated to insights, trends, and opinions from the world of data-driven technologies. It monitors developments, recognition, and achievements made by Artificial Intelligence, Big Data and Analytics companies across the globe.

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AI is Transforming the Modern Tech Industry in More Ways Than One - Analytics Insight

In heavily regulated industries such as healthcare, digital innovation can be slow to progress. However, once organizations push towards digital transformation and innovation, the benefits that can be achieved such as revenue growth, patient volume, and cost of care can provide tremendous value. Healthcare organizations are looking for an approach to cost-effective and technically efficient build-out to help on their digital transformation journeys. With investments shifting from core EMRs to infrastructure solutions that enable flexibility and adaptability, healthcare organizations are looking to digital innovation to solve these key issues. In an upcoming Enterprise Data &AI presentation on May 5, 2022, Vignesh Shetty, SVP & GM Edison AI And Platform, GE Healthcare Digital will discuss GE Healthcares digital health platform and how its helping companies in the healthcare sector on their AI and data journey.

Vignesh Shetty, SVP & GM Edison AI and Platform, GE Healthcare Digital

In this interview for Forbes, Vignesh shares how GE Healthcare is applying AI and ML, some of the challenges associated in adopting transformative technology in heathcare, as well as some of the things to consider when navigating privacy, trust, and security around data related use cases and needs.

How is GE Healthcare applying AI/ML in different application areas?

Vignesh Shetty: GE Healthcare uses AI to help healthcare providers achieve clinical and operational outcomes that create impacts for patients, providers, and health systems. For AI to be most effective, it should be seamless, invisible and within existing workflows while uncovering patterns (e.g., uncovering unknown unknowns) that are missed by humans.

Three areas where we see opportunities to apply AI are:

Platform as an AI engine: Healthcare systems experience fragmentation due to disjointed data sources, separate systems with incompatible vendors and other collection and collation issues. This digital friction makes it difficult for healthcare systems to adopt the applications and technology needed to access and manage enormous amounts of disparate clinical, diagnostic, and operational data.

We are developing Edison Digital Health Platform to accelerate app development and integration by connecting devices and other data sources into an aggregated clinical data layer. The goal of the platform is to enable hospitals and healthcare systems to effectively deploy the clinical, workflow, analytics and AI tools that support the improvement of care delivery, the promotion of high-efficiency operations, and supporting reduction in the IT burden that typically comes with installing and integrating apps across the enterprise.

For example: Edison Open AI Orchestrator simplifies the selection, deployment, and usage of multi-vendor AI in both departmental and healthcare enterprise workflows at scale.

Ondevice AI:

From big iron MRI scanners used by doctors to detect tumors on the prostate gland to mobile X-ray units in the ER or ICU that technicians use to image the lungs of COVID patients at their bedside, we are seeing a tangible impact with our AI embedded on the device.

Examples include:

Critical Care Suite which automatically analyzes X-Ray images for critical findings (such as pneumothorax) producing triage notifications. It also enables automated measurements and quality control that can help improve efficiency on the front lines.

Air Recon DL is our advanced deep learning Image Reconstruction Technology that works across anatomies this technology can offer clinicians a significant reduction in exam times, which helps with the patient experience and address todays backlog more quickly and with impressive image quality.

TrueFidelity CT uses deep-learning image reconstruction to generate razor-sharp with deep detail, true texture, and high fidelity for every CT scan.

Predictive insights at the department and enterprise level applications:

Early adopters have reported seeing significant reduction in no-show rates using the Smart Scheduling application which means more slots filled, greater efficiency for providers and payers, and a better experience for patient.

How do you identify which problem area(s) to start with for your data analytics and cognitive technology projects?

Vignesh Shetty: If you don't see AIs incredible potential to help healthcare providers improve diagnostic confidence, efficiency, and productivity, look closer. Likewise, if you don't find some of the hype absurd, look even closer.

GEHC invests a lot of time to avoid potential pitfalls by:

We work closely to collaborate on data and expertise between the two worlds of practitioners and our developers. Both are passionately striving to solve the same problems but not necessarily talking to each other, early enough. The result is that some offerings do not address the right clinical or operational need, are not suitably integrated into existing workflow, or simply do not work.

As a global leading med tech and digital provider, we are committed to helping healthcare providers reduce pain points, improve diagnostic confidence, and focus on reducing digital friction.

What are some of the unique opportunities you have when it comes to data and AI?

Vignesh Shetty: Folks call data the 21st century oil a better analogy would be crude oil. If harnessed well there is massive potential especially by focusing on these three areas:

AI, like other tools, is a new lever. Leverage by definitions amplifies an input to provide greater output. We are using data to understand the leverage points in a clinicians workflow which helps identify where to apply various tools (AI being one of several) to yield nonlinear results.

Can you share some of the challenges when it comes to AI and ML adoption, especially for heavily regulated industries such as healthcare?

Vignesh Shetty: The head of radiology at a hospital in Europe, and one of our key customers, used this description as it relates to AI when he said, The menu is spectacular, the spread is broad, the chefs are Michelin starred, the aroma is great, when do I get to eat?

His sense of unfulfilled potential stems from the following learnings:

In heavily regulated industries like healthcare, clinicians rely on heuristics and habit formation by constructing workflows that are unique to them to minimize mistakes.

For many physicians, the main hurdle to AI adoption is familiarity and experience with the technology while minimizing risk to the patient and distraction to ensure the AI is going to help rather than hinder their clinical routine. It's a quandary thats being resolved with thoughtful, targeted AI based on longitudinal patient data that builds trust and is quietly working behind the scenes so as not to disrupt or create another step in an already strained environment. Trust leads to utilization, which is a key to unleash AI's true potential.

How do you deal with varying levels of data quality for AI and ML systems?

Vignesh Shetty:

How are you navigating privacy, trust, and security concerns around the use of your data?

Vignesh Shetty: When it comes to deployment, an important hurdle is how to ensure safety and efficacy over time as algorithms adapt and evolve, through the continual evaluation of performance and assessing the need for reapprovals of specific AI solutions.

Healthcare providers and AI companies like ours are coming together to put in place robust data governance, ensuring interoperability and standards for data formats, enhance data security and bring clarity to consent over data sharing. Collaborating on cybersecurity expertise is key because it will largely influence the trajectory of AI adoption. The necessity of HIPAA and HI Trust* compliance as well as evolving privacy regulations make the standard for service very high.

AI research needs to heavily emphasize explainable, causal, and ethical AI, which could be a key driver of adoption.

What are you doing to develop a data literate and AI ready workforce?

Vignesh Shetty: At GE Healthcare, we are focused on thoughtful integration of ML and AI throughout the fabric of the organization using a three-tiered approach

We are optimistic about the future of AI, but we cant leave it to chance. Im convinced that the skills for responsible leadership in the AI era can be taught and that people can build safe and effective systems wisely.

What AI technologies are you most looking forward to in the coming years?

Vignesh Shetty: AI is central to building a future where healthcare is personalized, prevention-oriented, and affordable and we can make a difference to patients and providers in the moments that matter by offering both prescriptive and predictive AI driven insights to help healthcare providers improve both clinical & operational workflows.

Its possible to envision a significant improvement in the patient/provider experience using multi-modal data that create a longitudinal patient record which helps healthcare providers to schedule a patient at the right time which would reduce no-shows, ensure that patients are scheduled on the right device and facility with the relevant logistics in place. Imaging a patient receiving proactive care (thanks to wearables and sensors interacting with AI models) and enjoying frictionless experiences (with robotic assistants for routine tasks), all while going about her daily life.

This will not occur by applying new technologies through the lens of old applications or existing ways of doing things. Building a better mousetrap is a great way to onramp users into the digital realm. But it also has limitations; you can only see whats new in terms of what has always been.

The way forward will be native applications that are built with these new paradigms in mind. In retrospect, native applications can seem obvious, but in their early stages they can be difficult to imagine. The goal is to enable caregivers to get better, which means spending more time managing their patients rather than managing the patient record.

Lastly, bet right and early, when everyone (or most) others bet wrong, and try to build something people will look for, will talk about or would miss if it were gone.

In an upcoming Enterprise Data &AI presentation on May 5, 2022, Vignesh will dig deeper into some of the topics discussed above as well as share how GE Healthcares digital health platform is helping companies in the healthcare sector on their AI and data journey.

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The Truth About AI In Healthcare - Forbes