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Mention artificial intelligence to pretty much anyone and there's a good chance that the term that once seemed magical now spawns a queasy feeling. It generates thoughts of a computer stealing your job, technology companies spying on us, and racial, gender and economic bias.

So, how do we bring the magic back to AI? Maybe it comes down to people and things that humans actually do pretty well: thinking and planning. That's one finding that will become clear in a review of the articles in this Quick Study packed with InformationWeek articles focused on AI ethics and bias.

Yes, there are ways to develop and utilize AI in ethical manners, but they involve thinking through how your organization will use AI, how you will test it, and what your training data looks like. In these articles AI experts and companies that have succeeded with AI share their advice.

What You Need to Know About AI Ethics

Honesty is the best policy. The same is true when it comes to artificial intelligence. With that in mind, a growing number of enterprises are starting to pay attention to how AI can be kept from making potentially harmful decisions.

Why AI Ethics Is Even More Important Now

Contact-tracing apps are fueling more AI ethics discussions, particularly around privacy. The longer term challenge is approaching AI ethics holistically.

Data Innovation in 2021: Supply Chain, Ethical AI, Data Pros in High Demand

Year in Review: In year two of the pandemic, enterprise data innovation pros put a focus on supply chain, ethical AI, automation, and more. From the automation to the supply chain to responsible/ethical AI, enterprises made progress in their efforts during 2021, but more work needs to be done.

The Tech Talent Chasm

How a changing world is forcing businesses to rethink everything, and in recruiting IT talent understand that great candidates want their employers to take AI ethics seriously.

3 Components CIOs Need to Create an Ethical AI Framework

CIOs shouldnt wait for an ethical AI framework to be mandatory. Whether buying the technology or building it, they need processes in place to embed ethics into their AI systems, according to PwC.

Why You Should Have an AI & Ethics Board

Guidelines are great -- but they need to be enforced. An ethics board is one way to ensure these principles are woven into product development and uses of internal data, according to the chief data officer of ADP.

How and Why Enterprises Must Tackle Ethical AI

Artificial intelligence is becoming more common in enterprises, but ensuring ethical and responsible AI is not always a priority. Here's how organizations can make sure that they are avoiding bias and protecting the rights of the individual.

Common AI Ethics Mistakes Companies Are Making

More organizations are embracing the concept of responsible AI, but faulty assumptions can impede success.

How IT Pros Can Lead the Fight for Data Ethics

Maintaining ethics means being alert on a continuum for issues. Heres how IT teams can play a pivotal role in protecting data ethics.

Ex-Googler's Ethical AI Startup Models More Inclusive Approach

Backed by big foundations, ethical AI startup DAIR promises a focus on AI directed by and in service of the many rather than controlled just by a few giant tech companies. How do its goals align with your enterprise's own AI ethics program?

The Cost of AI Bias: Lower Revenue, Lost Customers

A survey shows tech leadership's growing concern about AI bias and AI ethics, as negative events impact revenue, customer losses, and more.

What We Can Do About Biased AI

Biased artificial intelligence is a real issue. But how does it occur, what are the ramifications -- and what can we do about it?

How Fighting AI Bias Can Make Fintech Even More Inclusive

Digitized presumptions, encoded by very human creators, can introduce prejudice in new financial technology meant to be more accessible.

Im Not a Cat: The Human Side of Artificial Intelligence

Unconscious biases will be reflected in the data that feeds your AI and ML algorithms. Here are three simple actions to dismantle unconscious bias in AI.

When A Good Machine Learning Model Is So Bad

IT teams must work with managers who oversee data scientists, data engineers, and analysts to develop points of intervention that complement model ensemble techniques.

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Quick Study: Artificial Intelligence Ethics and Bias - InformationWeek

A new study finds that an artificial intelligence model can predict whether Crohns disease will recur after surgery.

A deep learning model trained to analyze histological images of surgical specimens accurately classified patients with and without Crohns disease recurrence, investigators report in The American Journal of Pathology.

According to researchers, more than 500,000 individuals in the United States have Crohns disease. Crohns disease is a chronic inflammatory bowel disease that damages the digestive system lining. It can cause digestive system inflammation, which may result in abdominal pain, severe diarrhea, exhaustion, weight loss, and malnutrition.

Many people end up needing surgery to treat their Crohns disease. Even after a successful operation, recurrence is common. Now, researchers are reporting that their AI tool is highly accurate at predicting the postoperative recurrence of Crohns disease. It also linked recurrence with the histology of subserosal adipose cells and mast cell infiltration.

Using an artificial intelligence (AI) tool that simulates how humans visualize and is trained to identify and categorize pictures, researchers created a model that predicts the postoperative recurrence of Crohns disease with high accuracy by evaluating histological images. The AI tool also identified previously unknown differences in adipose cells and substantial disparities in the degree of mast cell infiltration in the subserosa, or outer lining of the gut, when comparing individuals with and without disease recurrence. Elseviers The American Journal of Pathology published the findings.

The 10-year rate of postoperative symptomatic recurrence of Crohns disease, a chronic inflammatory gastrointestinal illness, is believed to be 40%. Although there are scoring methods to measure Crohns disease activity and the existence of postoperative recurrence, no scoring system has been devised to predict whether Crohns disease will return.

Sixty-eight patients with Crohns disease were classified according to the presence or absence of postoperative recurrence within two years. The investigators performed histological analysis of surgical specimens using deep learning EfficientNet-b5, a commercially available AI model designed to perform image classification. They achieved a highly accurate prediction of postoperative recurrence (AUC=0.995) and discovered morphological differences in adipose cells between the two groups. Credit: The American Journal of Pathology

Most of the analysis of histopathological images using AI in the past have targeted malignant tumors, explained lead investigators Takahiro Matsui, MD, Ph.D., and Eiichi Morii, MD, Ph.D., Department of Pathology, Osaka University Graduate School of Medicine, Osaka, Japan. We aimed to obtain clinically useful information for a wider variety of diseases by analyzing histopathology images using AI. We focused on Crohns disease, in which postoperative recurrence is a clinical problem.

The research involved 68 Crohns disease patients who underwent bowel resection between January 2007 and July 2018. They were divided into two groups based on whether or not they had postoperative disease recurrence within two years after surgery. Each group was divided into two subgroups, one for training and the other for validation of an AI model. Whole slide pictures of surgical specimens were cropped into tile images for training, labeled for the presence or absence of postsurgical recurrence, and then processed using EfficientNet-b5, a commercially available AI model built to perform image classification. When the model was tested with unlabeled photographs, the findings indicated that the deep learning model accurately classified the unlabeled images according to the presence or absence of disease occurrence.

Following that, prediction heat maps were created to identify areas and histological features from which the machine learning algorithm could accurately predict recurrence. All layers of the intestinal wall were shown in the photos. The heatmaps revealed that the machine learning algorithm correctly predicted the subserosal adipose tissue layer. However, the model was less precise in other regions, such as the mucosal and proper muscular layers. Images with the greatest accurate predictions were taken from the non-recurrence and recurrence test datasets. The photos with the greatest predictive results all had adipose tissue.

Because the machine learning model achieved accurate predictions from images of subserosal tissue, the investigators hypothesized that subserosal adipose cell morphologies differed between the recurrence and the non-recurrence groups. Adipose cells in the recurrence group had a significantly smaller cell size, higher flattening, and smaller center-to-center cell distance values than those in the nonrecurrence group.

These features, defined as adipocyte shrinkage, are important histological characteristics associated with Crohns disease recurrence, said Dr. Matsui and Dr. Morii.

The investigators also hypothesized that the differences in adipocyte morphology between the two groups were associated with some degree or type of inflammatory condition in the tissue. They found that the recurrence group had a significantly higher number of mast cells infiltrating the subserosal adipose tissue, indicating that the cells are associated with the recurrence of Crohns disease and the adipocyte shrinkage phenomenon.

To the investigators knowledge, these findings are the first to link postoperative recurrence of Crohns disease with the histology of subserosal adipose cells and mast cell infiltration. Dr. Matsui and Dr. Morii observed, Our findings enable stratification by the prognosis of postoperative Crohns disease patients. Many drugs, including biologicals, are used to prevent Crohns disease recurrence, and proper stratification can enable more intensive and successful treatment of high-risk patients.

Reference: Deep Learning Analysis of Histologic Images from Intestinal Specimen Reveals Adipocyte Shrinkage and Mast Cell Infiltration to Predict Postoperative Crohn Disease by Hiroki Kiyokawa, Masatoshi Abe, Takahiro Matsui, Masako Kurashige, Kenji Ohshima, Shinichiro Tahara, Satoshi Nojima, Takayuki Ogino, Yuki Sekido, Tsunekazu Mizushima and Eiichi Morii, 28 March 2022, The American Journal of Pathology.DOI: 10.1016/j.ajpath.2022.03.006

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Artificial Intelligence Model Can Successfully Predict the Reoccurrence of Crohns Disease - SciTechDaily

A new study finds that utilizing artificial intelligence could allow scientists to detect arthritis earlier.

Researchers have been able to teach artificial intelligence neural networks to distinguish between two different kinds of arthritis and healthy joints. The neural network was able to detect 82% of the healthy joints and 75% of cases of rheumatoid arthritis. When combined with the expertise of a doctor, it could lead to much more accurate diagnoses. Researchers are planning to investigate this approach further in another project.

This breakthrough by a team of doctors and computer scientists has been published in the journal Frontiers in Medicine.

There are many different varieties of arthritis, and determining which type of inflammatory illness is affecting a patients joints may be difficult. Computer scientists and physicians from Friedrich-Alexander-Universitt Erlangen-Nrnberg (FAU) and Universittsklinikum Erlangen have now taught artificial neural networks to distinguish between rheumatoid arthritis, psoriatic arthritis, and healthy joints in an interdisciplinary research effort.

Within the scope of the BMBF-funded project Molecular characterization of arthritis remission (MASCARA), a team led by Prof. Andreas Maier and Lukas Folle from the Chair of Computer Science 5 (Pattern Recognition) and PD Dr. Arnd Kleyer and Prof. Dr. Georg Schett from the Department of Medicine 3 at Universittsklinikum Erlangen was tasked with investigating the following questions: Can artificial intelligence (AI) recognize different forms of arthritis based on joint shape patterns? Is this strategy useful for making more precise diagnoses of undifferentiated arthritis? Is there any part of the joint that should be inspected more carefully during a diagnosis?

Currently, a lack of biomarkers makes correct categorization of the relevant form of arthritis challenging. X-ray pictures used to help diagnosis are also not completely trustworthy since their two-dimensionality is insufficiently precise and leaves room for interpretation. This is in addition to the challenge of placing the joint under examination for X-ray imaging.

To find the answers to its questions, the research team focused its investigations on the metacarpophalangeal joints of the fingers regions in the body that are very often affected early on in patients with autoimmune diseases such as rheumatoid arthritis or psoriatic arthritis. A network of artificial neurons was trained using finger scans from high-resolution peripheral quantitative computer tomography (HR-pQCT) with the aim of differentiating between healthy joints and those of patients with rheumatoid or psoriatic arthritis.

HR-pQCT was selected as it is currently the best quantitative method of producing three-dimensional images of human bones in the highest resolution. In the case of arthritis, changes in the structure of bones can be very accurately detected, which makes precise classification possible.

A total of 932 new HR-pQCT scans from 611 patients were then used to check if the artificial network can actually implement what it had learned: Can it provide a correct assessment of the previously classified finger joints?

The results showed that AI detected 82% of the healthy joints, 75% of the cases of rheumatoid arthritis, and 68% of the cases of psoriatic arthritis, which is a very high hit probability without any further information. When combined with the expertise of a rheumatologist, it could lead to much more accurate diagnoses. In addition, when presented with cases of undifferentiated arthritis, the network was able to classify them correctly.

We are very satisfied with the results of the study as they show that artificial intelligence can help us to classify arthritis more easily, which could lead to quicker and more targeted treatment for patients. However, we are aware of the fact that there are other categories that need to be fed into the network. We are also planning to transfer the AI method to other imaging methods such as ultrasound or MRI, which are more readily available, explains Lukas Folle.

Whereas the research team was able to use high-resolution computer tomography, this type of imaging is only rarely available to physicians under normal circumstances because of restraints in terms of space and costs. However, these new findings are still useful as the neural network detected certain areas of the joints that provide the most information about a specific type of arthritis which is known as intra-articular hotspots. In the future, this could mean that physicians could use these areas as another piece in the diagnostic puzzle to confirm suspected cases, explains Dr. Kleyer. This would save time and effort during the diagnosis and is already in fact possible using ultrasound, for example. Kleyer and Maier are planning to investigate this approach further in another project with their research groups.

Reference: Deep Learning-Based Classification of Inflammatory Arthritis by Identification of Joint Shape PatternsHow Neural Networks Can Tell Us Where to Deep Dive Clinically by Lukas Folle, David Simon, Koray Tascilar, Gerhard Krnke, Anna-Maria Liphardt, Andreas Maier, Georg Schett and Arnd Kleyer, 10 March 2022, Frontiers in Medicine.DOI: 10.3389/fmed.2022.850552

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Early Detection of Arthritis Now Possible Thanks to Artificial Intelligence - SciTechDaily

SPOILER ALERT: Do not read unless you have watched Top Gun: Maverick, in theaters now.

Top Gun fans knew ahead of time that Val Kilmer would be reprising his role of Tom Iceman Kazansky in the sequel, but the specifics of the actors return were a question mark considering Kilmer lost the ability to speak after undergoing throat cancer treatment in 2014. The script for Top Gun: Maverick pulls from Kilmers real life, with Iceman also having cancer and communicating through typing. Kilmer gets to say one brief line of dialogue. In real life Kilmers speaking voice has been revived courtesy of artificial intelligence.

Kilmer announced in August 2021 that he had partnered with Sonantic to create an A.I.-powered speaking voice for himself. The actor supplied the company with hours of archival footage featuring his speaking voice that was then fed through the companys algorithms and turned into a model. According to Fortune, this process was used again for the actors Top Gun: Maverick appearance. However, a studio sources tells Variety no A.I. was used in the making of the movie.

In the end, we generated more than 40 different voice models and selected the best, highest-quality, most expressive one, John Flynn, CTO and cofounder of Sonantic, said in a statement to Forbes about reviving Kilmers voice. Those new algorithms are now embedded into our voice engine, so future clients can automatically take advantage of them as well.

Im grateful to the entire team at Sonantic who masterfully restored my voice in a way Ive never imagined possible, Kilmer originally said in a statement about the A.I. As human beings, the ability to communicate is the core of our existence and the side effects from throat cancer have made it difficult for others to understand me. The chance to narrate my story, in a voice that feels authentic and familiar, is an incredibly special gift.

As Fortune reports: After cleaning up old audio recordings of Kilmer, [Sonantic] used a voice engine to teach the voice model how to speak like Kilmer. The engine had around 10 times less data than it would have been given in a typical project, Sonantic said, and it wasnt enough. The company then decided to come up with new algorithms that could produce a higher-quality voice model using the available data.

Top Gun: Maverick is now playing in theaters nationwide.

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Val Kilmers Return: A.I. Created 40 Models to Revive His Voice Ahead of Top Gun: Maverick - Variety

A research group from the Graduate School of Informatics, Nagoya University, has taken a big step towards creating a neural network with metamemory through a computer-based evolution experiment.

In recent years, there has been rapid progress in designing artificial intelligence technology using neural networks that imitate brain circuits. One goal of this field of research is understanding the evolution of metamemory to use it to create artificial intelligence with a human-like mind.

Metamemory is the process by which we ask ourselves whether we remember what we had for dinner yesterday and then use that memory to decide whether to eat something different tonight. While this may seem like a simple question, answering it involves a complex process. Metamemory is important because it involves a person having knowledge of their own memory capabilities and adjusting their behavior accordingly.

In order to elucidate the evolutionary basis of the human mind and consciousness, it is important to understand metamemory, explains lead author Professor Takaya Arita. A truly human-like artificial intelligence, which can be interacted with and enjoyed like a family member in a persons home, is an artificial intelligence that has a certain amount of metamemory, as it has the ability to remember things that it once heard or learned.

When studying metamemory, researchers often employ a delayed matching-to-sample task. In humans, this task consists of the participant seeing an object, such as a red circle, remembering it, and then taking part in a test to select the thing that they had previously seen from multiple similar objects. Correct answers are rewarded and wrong answers punished. However, the subject can choose not to do the test and still earn a smaller reward.

A human performing this task would naturally use their metamemory to consider if they remembered seeing the object. If they remembered it, they would take the test to get the bigger reward, and if they were unsure, they would avoid risking the penalty and receive the smaller reward instead. Previous studies reported that monkeys could perform this task as well.

The Nagoya University team comprising Professor Takaya Arita, Yusuke Yamato, and Reiji Suzuki of the Graduate School of Informatics created an artificial neural network model that performed the delayed matching-to-sample task and analyzed how it behaved.

Despite starting from random neural networks that did not even have a memory function, the model was able to evolve to the point that it performed similarly to the monkeys in previous studies. The neural network could examine its memories, keep them, and separate outputs. The intelligence was able to do this without requiring any assistance or intervention by the researchers, suggesting the plausibility of it having metamemory mechanisms.The need for metamemory depends on the user's environment. Therefore, it is important for artificial intelligence to have a metamemory that adapts to its environment by learning and evolving, says Professor Arita of the finding. The key point is that the artificial intelligence learns and evolves to create a metamemory that adapts to its environment.

Creating an adaptable intelligence with metamemory is a big step towards making machines that have memories like ours. The team is enthusiastic about the future, This achievement is expected to provide clues to the realization of artificial intelligence with a human-like mind and even consciousness.

The research results were published in the online edition of the international scientific journal Scientific Reports. The study was partly supported by a JSPS/MEXT Grants-in-Aid for Scientific Research KAKENHI (JP17H06383 in #4903).

Scientific Reports

Evolution of metamemory based on self-reference to own memory in artificial neural network with neuromodulation

Disclaimer: AAAS and EurekAlert! are not responsible for the accuracy of news releases posted to EurekAlert! by contributing institutions or for the use of any information through the EurekAlert system.

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Creating artificial intelligence that acts mo - EurekAlert