Category Archives: Artificial Intelligence

Molecular findings

The variants occurred de novo in 12 individuals, were maternally inherited in Individuals K and L, and paternally inherited in individual O. One parent of affected Individual T, Individual U, showed a low level of mosaicism for the variant (with only 2 out of 298 sequencing reads for this variant found in her blood). Nine individuals had unknown modes of inheritance. A majority, 20, are truncating variants (frameshift or nonsense), and five are missense (with three of five belonging to the same family). Twenty-one distinct variants were identified (Table1), with locations shown in Fig.2 [18].

The coding exons for ANKRD11 are depicted to scale. Abbreviations: aa amino acid. The figure was made using: https://www.cbioportal.org/mutation_mapper.

Truncating variants are classified by ACMG criteria [19] as: PVS1 null variant (nonsense, frameshift) in a gene where loss of function is a known mechanism of disease. Some variants are classified as PS2 De novo (both maternity and paternity confirmed) in a patient with the disease and no family history. One missense variants in our cohort (p. (Val586Met) was seen in a heterozygous control individual in the Genome Aggregation Database (GnomAD), thus calling into question its pathogenicity. It is also formally possible that the one individual in GnomAD might be mildly affected. The mother with this variant (individual M) has a very mild phenotype whereas her children (individuals K and L) have phenotypes more consistent with KBG syndrome. However, a recent preprint [20] demonstrated that some missense variants do impair ANKRD11 ability and/or stability, but that these variants mainly localize in the Repression Domain 2. Those authors also tested one variant in the Repression domain 1 (p.Leu509Pro), which turned out to have no effect on ANKRD11 stability or activity. The p.(Val586Met) variant of individuals K, L, and M also falls within the Repression Domain 1, and it has a borderline CADD score (23.9) and is not as highly conserved as the other missense variants. In addition, the affected nucleotides and corresponding amino acid are also not highly conserved when the sequence is aligned with other species. Per DeepGestalt, these individuals (K, L, M) did not have KBG syndrome listed in their top 30 differentials. Segregation analysis with the mother and sister of Individual M is not yet available. While the mother has very mild clinical features of KBG syndrome, the sister (aunt of Individuals K and L) is potentially reporting more severe symptoms. Ultimately, the pathogenicity of the variant (p.(Val586Met)) is still uncertain.

A different missense variant (p. Arg2536Gln) arose de novo and was initially classified as a variant of uncertain significance because it had not been previously reported. However, it has been reclassified because of new information available: two additional patients carrying the variant. One is reported in Clinvar (https://www.ncbi.nlm.nih.gov/clinvar/variation/1012410/?new_evidence=false), a patient in whom the variant was maternally inherited (referred to as Individual Z in Supplementary Information), but who was unavailable for videoconferencing. In the other previously reported patient, the variant has arisen de novo and was classified as pathogenic [21]. Although a more extensive cosegregation of the patient reported in Clinvar is not available, since phenotypes characteristic of KBG syndrome are seen in three individuals possessing this variant, the variant is reclassified to likely pathogenic. Further details about these cases can be found in Supplemental Text and Case Summaries.

As of April 2022, there are 429 putative missense or non-frameshift deletion, substitution or insertion variants in ANKRD11 submitted to ClinVar [22], with many of these listed as variants of uncertain significance (Supplementary Table2), with bioinformatic analyses providing a suggested consensus classification for each variant.

Median age of the 25 individuals was 11 years and average age was 15 years (range=159). One comes from a consanguineous family, roughly half (n=12) had a history of congenital abnormalities in the family, and eight had relatives with intellectual disabilities.

The parents of individuals B, D, T, and Y had histories of miscarriage. The variant was de novo for individual B, whereas the parent of individual T (Individual U) was mosaic for the missense variant (as noted above). The mother of individual Z has a history of several miscarriages early in pregnancy around six weeks of age. The inheritance pattern is unknown for individuals D and Y.

The parents in this study (M, P, U) generally had mild phenotypic features. Individual M, the mother of K and L, possessed some distinct facial traits (e.g., thick eyebrows, anteverted nares, broad nasal base), however, the overall constellation of features was not typical of KBG syndrome. She did not present with common features such as developmental delay, macrodontia, or short stature. Conversely, individual P, the father of O, presented with global developmental delay, macrodontia, and short stature among other common traits of KBG syndrome. Lastly, individual U, the mother of T, had mild facial features (e.g., synophrys, thick eyebrow, wide nasal bridge, prominent nasal tip) with speech delays and seizures in childhood.

The overall frequency of certain phenotypic features is shown in Table2, and these are reviewed in further detail in the following sections.

Height at the time of videoconference clustered into 398th centile (44%), below 3rd centile (24%) and above 98th centile (12%) with a median height of 140.0 29.4cm. Weights at time of videoconference clustered into 3-98th centile (48%), below the 3rd centile (20%), and above 98th centile (4%), with a median weight of 27.8 29.1kg. Of the three individuals who had heights above the 98th centile at time of videoconference, one had been put on growth hormone for approximately 24 years (Individual J) (Table3). Birth length clustered into 398th centile (44%), above 98th centile (8%), and below 3rd centile (8%), with a median length of 49.0 6.3cm. Birth weight clustered between 398th centile (64%), and below the 3rd centile (16%) with a median birth weight of 3 0.7kg.

The photographs with permission for publication are shown in Fig.3. At least one distinctive facial feature common to KBG patients was present in every individual interviewed. Defining facial characteristics include thick eyebrows with synophrys, prominent eyelashes, wide nose, thin upper lip vermillion, and macrodontia. Many have a triangular face or pointed chin and a broad or prominent forehead.

Characteristic features include bushy eyebrows (A, C, D, E, I, K, M, O, P, R, T, U, V, Y), long eyelashes (C, D, I, L, O, P, S, X,), triangular face (A, G, K, R, V) and most had a wide nasal bridge or tip and a thin upper vermillion.

Pairwise ranks of the 25 photos in Fig.4 suggest most patients described in this analysis share similar facial phenotypes. In a gallery of 3533 images with 816 different disorders and 25 KBG patients, 15 out of 25 KBG patients had at least one other KBG patient in their top-10 rank, and 21 out of 25 patients had at least one other patient in their top-30 rank. Other than U being an outlier, there was a cluster containing the set of patients with three sub-clusters (P, J, F, and M), (O, H, R, Y, V, G, and I), and (Q, S, D, and E). Patient U was an outlier, perhaps due to the low-level mosaicism for this variant. No clear clusters were seen when segregated by type of genetic variant (missense, frameshift, nonsense). The similarity between family members is a known confounder in the analysis of syndromic similarity. On average, family members with the same disorder are closer in the clinical face phenotype space than unrelated individuals with the same disorder. That said, in one family, we do not see an increased similarity between M, K, and L.

Sub-cluster P, J, F, M present with synophrys and wide noses. Sub-cluster O, H, R, Y, V, G, I present with thick eyebrows, prominent/broad nasal tips, macrodontia, triangular faces and pointed chins. Sub-cluster Q, S, D, E present with anteverted nares, broad nasal tips, and macrodontia. Link: https://db.gestaltmatcher.org/; individual links to each patient in Supplemental Text. Note: Individual E did not consent to having their photo published, however, a frontal photo was input into the GestaltMatcher and DeepGestalt algorithms.

KBG syndrome was recommended among the top 30 syndromes and ranked as the first (i.e., most likely) diagnosis for 28% (n=7) of individuals, second for 40% (n=10), and third or fourth for 12% (n=3). Overall, 80% (n=20) of patients photos analyzed had KBG syndrome ranked in their top-five potential diagnoses out of the 30 possible suggested syndromes from among the 300+ syndromes currently recognized by the DeepGestalt algorithm. Among the 20 with KBG in the top-five rank, seven had a high gestalt score, 10 had medium gestalt, and three had low gestalt. Fourteen had a medium feature score, five had a low score, and one was unranked for features of KBG (see Supplementary Table3). Individuals B, F, and J initially submitted photos where they were wearing glasses. After analyzing photos without glasses, the ranking of KBG surprisingly dropped from two to six for individual B and from two to three for individual J. Ranking did not change for individual F. While KBG ranking fluctuated, the gestalt and feature levels did not change between the photos with and without glasses for any of the three individuals.

Five individuals (K, L, M, P, U) did not have KBG syndrome appear as a differential diagnosis out of 30. First ranked diagnoses instead included Cornelia de Lange, Williams-Beuren, Rubinstein-Taybi, Angelman, and mucopolysaccharidosis. Notably, Individual P was 5560 years old at the time of the videoconference whereas Individual U was 3035 years old, and both of them initially submitted pictures of themselves around those ages. These ages fall above our median age of 11 years and the age at which most individuals are diagnosed with KBG syndrome. DeepGestalt relies on the photos that it is trained on, so older age photos may not perform as well. Additionally, individual U has very low-level mosaicism for this variant, potentially resulting in lower phenotypic expression of facial features. The other three individuals who were unranked (K, L, and M) are all from the same family and possess the same missense variant (Table1) with questionable pathogenicity.

With PEDIA score, the disease-causing gene ANKRD11 is ranked at the first place in 18 out of 25 (top-1 accuracy: 72%). When looking at the top-10 genes, ANKRD11 is listed in the top-10 genes in 22 out of 25 (top-10 accuracy: 88%). All have ANKRD11 in their top-30 genes.

Eight reported an intelligence quotient (IQ) score, with a mean of 734.84 (range=6480) as measured by the Weschler Intelligence Scale (3rd to 5th edition). A majority, 68% are considered mildly to moderately intellectually disabled based on level of functioning. Global developmental delays prior to 5 years were seen in 68% (n=17), with nine being classified as mild. Median age of crawling onset was 12 months (range=924) (n=8), walking onset 22 months (range=12.536) (n=10), and speech onset 30 months (range=1936) (n=6). Selective mutism and absent speech were observed in three individuals.

Common types of seizures reported included myoclonic, tonic-clonic, and absence with no specific type predominating [23]. Electroencephalogram (EEG) abnormalities were documented in three of 11 individuals with seizures. According to maternal report, Individual E was meeting speech and motor milestones until the onset of myoclonic seizures, complex partial seizures, and verbal tonic seizures with respiratory distress around 0.52 years of age. Similarly, individuals H, K, R, S, T, U, X, and Y reported histories of various types of seizures and concurrent speech and motor delays. Brain abnormalities detected on magnetic resonance imaging (MRI) included pineal cyst, arachnoid cyst, choroid plexus cyst, subdural hemorrhage, and small pituitary gland.

Abnormal mood included abnormal emotion or affect, depression, and/or anxiety, self-injurious behavior including self-biting. Individuals E, O, Q, and R report absent or high pain threshold. O has a history of a fractured foot and a dislocated kneecap with bone scans showing normal density. Impaired tactile sensation was reported in two individuals (M,S).

Six had chronic otitis media, with five of six having concurrent hearing impairment. Those experiencing chronic otitis media likewise had a preauricular pit, abnormal or blocked Eustachian tubes, abnormality of the tympanic membrane, enlarged vestibular aqueduct, choanal atresia, and increased size of nasopharyngeal adenoids. Hearing loss and recurrent infections including sinus, chronic ear, and upper respiratory infections were present in four individuals (O, P, Q, Y). Of the six with palatal anomalies, four had difficulties feeding.

Of note, individual A was diagnosed with osteopenia, and later osteoporosis, at 1520 years with low bone mineral densitometry in the lumbar spine, hip, and femoral neck. An x-ray of his left hand and wrist was performed which revealed physeal closure of the bones, excluding delayed bone maturation. Individual S has visible sacral dimple and was referred to neurology for gait disturbance and urinary incontinency. MRI of her lumbar spine revealed a tethered spinal cord.

Cardiac abnormalities were seen in approximately half the participants and while many resolved without the need for surgical intervention, individual K had Tetralogy of Fallot with pulmonary valve-sparing surgical repair at ~36 months of age. Individual T had mitral valve repair at around one year of age.

Participants F, M, S, T, U had presumed diagnoses of abdominal migraines, characterized by stomach pain, nausea, and vomiting. In F, the abdominal migraines were accompanied by cyclic vomiting syndrome. Reports described her episode as significant pain causing writhing with soft, nontender abdomen normal bowel sounds on examination.

Short stature is a common phenotype in those with KBG syndrome with up to 66% below the 10th centile in height [5]. Individuals H, J, and O were administered growth hormone. J was born with a length below 1st centile and weight at 57th centile. After receiving somatropin injections from 3.5 years to 5.7 years of age, his height is at the 13th centile and weight is at 24th centile. O was given growth hormone from approximately 6 years to 11 with positive improvement in weight (11th percentile at birth and is now at 45th percentile). Efficacy of hormone supplementation is unknown for H. Reports of precocious puberty, immunodeficiency, recurring infections, allergies are also common.

Urogenital disorders were seen in 48% (n=12) of individuals, with seven being female and five being male. Of note, four males were diagnosed with cryptorchidism. Other diagnoses included abnormalities of the urethra and/or bladder, recurrent urinary tract infection, pollakiuria, polyuria, and enuresis.

A majority (56%) reported abnormalities of skin, nails, and hair, which included: hirsutism, low anterior hairline or abnormal hair whorl, cellulitis, keratosis pilaris, acne and dry skin, psoriasiform dermatitis, eczema, fingernail dysplasia, and recurrent fungal infections.

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KBG syndrome: videoconferencing and use of artificial intelligence driven facial phenotyping in 25 new patients | European Journal of Human Genetics -...

There are teams of researchers in academia and at major AI labs these days working on the problem of AI ethics, or the moral concerns raised by AI systems. These efforts tend to be especially focused on data privacy concerns and on what is known as AI bias AI systems that, using training data with bias often built in, produce racist or sexist results, such as refusing women credit card limits theyd grant a man with identical qualifications.

There are also teams of researchers in academia and at some (though fewer) AI labs that are working on the problem of AI alignment. This is the risk that, as our AI systems become more powerful, our oversight methods and training approaches will be more and more meaningless for the task of getting them to do what we actually want. Ultimately, well have handed humanitys future over to systems with goals and priorities we dont understand and can no longer influence.

Today, that often means that AI ethicists and those in AI alignment are working on similar problems. Improving the understanding of the internal workings of todays AI systems is one approach to solving AI alignment, and is crucial for understanding when and where models are being misleading or discriminatory.

And in some ways, AI alignment is just the problem of AI bias writ (terrifyingly) large: We are assigning more societal decision-making power to systems that we dont fully understand and cant always audit, and that lawmakers dont know nearly well enough to effectively regulate.

As impressive as modern artificial intelligence can seem, right now those AI systems are, in a sense, stupid. They tend to have very narrow scope and limited computing power. To the extent they can cause harm, they mostly do so either by replicating the harms in the data sets used to train them or through deliberate misuse by bad actors.

But AI wont stay stupid forever, because lots of people are working diligently to make it as smart as possible.

Part of what makes current AI systems limited in the dangers they pose is that they dont have a good model of the world. Yet teams are working to train models that do have a good understanding of the world. The other reason current systems are limited is that they arent integrated with the levers of power in our world but other teams are trying very hard to build AI-powered drones, bombs, factories, and precision manufacturing tools.

That dynamic where were pushing ahead to make AI systems smarter and smarter, without really understanding their goals or having a good way to audit or monitor them sets us up for disaster.

And not in the distant future, but as soon as a few decades from now. Thats why its crucial to have AI ethics research focused on managing the implications of modern AI, and AI alignment research focused on preparing for powerful future systems.

So do these two groups of experts charged with making AI safe actually get along?

Hahaha, no.

These are two camps, and theyre two camps that sometimes stridently dislike each other.

From the perspective of people working on AI ethics, experts focusing on alignment are ignoring real problems we already experience today in favor of obsessing over future problems that might never come to be. Often, the alignment camp doesnt even know what problems the ethics people are working on.

Some people who work on longterm/AGI-style policy tend to ignore, minimize, or just not consider the immediate problems of AI deployment/harms, Jack Clark, co-founder of the AI safety research lab Anthropic and former policy director at OpenAI, wrote this weekend.

From the perspective of many AI alignment people, however, lots of ethics work at top AI labs is basically just glorified public relations, chiefly designed so tech companies can say theyre concerned about ethics and avoid embarrassing PR snafus but doing nothing to change the big-picture trajectory of AI development. In surveys of AI ethics experts, most say they dont expect development practices at top companies to change to prioritize moral and societal concerns.

(To be clear, many AI alignment people also direct this complaint at others in the alignment camp. Lots of people are working on making AI systems more powerful and more dangerous, with various justifications for how this helps learn how to make them safer. From a more pessimistic perspective, nearly all AI ethics, AI safety, and AI alignment work is really just work on building more powerful AIs but with better PR.)

Many AI ethics researchers, for their part, say theyd love to do more but are stymied by corporate cultures that dont take them very seriously and dont treat their work as a key technical priority, as former Google AI ethics researcher Meredith Whittaker noted in a tweet:

The AI ethics/AI alignment battle doesnt have to exist. After all, climate researchers studying the present-day effects of warming dont tend to bitterly condemn climate researchers studying long-term effects, and researchers working on projecting the worst-case scenarios dont tend to claim that anyone working on heat waves today is wasting time.

You could easily imagine a world where the AI field was similar and much healthier for it.

Why isnt that the world were in?

My instinct is that the AI infighting is related to the very limited public understanding of whats happening with artificial intelligence. When public attention and resources feel scarce, people find wrongheaded projects threatening after all, those other projects are getting engagement that comes at the expense of their own.

Lots of people even lots of AI researchers do not take concerns about the safety impacts of their work very seriously.

Sometimes leaders dismiss long-term safety concerns out of a sincere conviction that AI will be very good for the world, so the moral thing to do is to speed full ahead on development.

Sometimes its out of the conviction that AI isnt going to be transformative at all, at least not in our lifetimes, and so theres no need for all this fuss.

Sometimes, though, its out of cynicism experts know how powerful AI is likely to be, and they dont want oversight or accountability because they think theyre superior to any institution that would hold them accountable.

The public is only dimly aware that experts have serious safety concerns about advanced AI systems, and most people have no idea which projects are priorities for long-term AI alignment success, which are concerns related to AI bias, and what exactly AI ethicists do all day, anyway. Internally, AI ethics people are often siloed and isolated at the organizations where they work, and have to battle just to get their colleagues to take their work seriously.

Its these big-picture gaps with AI as a field that, in my view, drive most of the divides between short-term and long-term AI safety researchers. In a healthy field, theres plenty of room for people to work on different problems.

But in a field struggling to define itself and fearing its not positioned to achieve anything at all? Not so much.

A version of this story was initially published in the Future Perfect newsletter. Sign up here to subscribe!

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AI bias and AI safety teams are divided on artificial intelligence - Vox.com

In February, UCI launched the Institute for Precision Health, a campus-wide, interdisciplinary endeavor that merges UCIs powerhouse health sciences, engineering, machine learning, artificial intelligence, clinical genomics and data science capabilities. The objective is to identify, create and deliver the most effective health and wellness strategy for each individual person and, in doing so, confront the linked challenges of health equity and the high cost of care.

IPH will bring a multifaceted, integrated approach to what many call the next great advancement in healthcare. The institute is an ecosystem for collaboration across disciplines.

Besides leading the applied artificial intelligence research group for IPH, Dr. Peter Chang is co-director of the Center for Artificial Intelligence in Diagnostic Medicine and assistant professor-in-residence in radiological sciences in the School of Medicine. Dr. Changs unique perspective arises from experience both as a radiologist physician and full-stack software engineer with over a decade of experience building FDA-cleared tools used in hospitals around the world.

Chang came to academia after launching a successful start-up company and has the distinction of being one of the few medical doctors in the country teaching in a computer science department. At IPH, his job is to use AI and machine learning to design practical solutions to real-world clinical problems for cost-effective, value-based care. Here, Dr. Chang speaks about the promise of AI and what he sees for the Institute for Precision Healths future.

For the uninitiated, how would you describe why machine learning and AI are important to healthcare right now?

You have to understand that the newest form of AI the deep-learning neural network family of algorithms has completely revolutionized the way machine-learning algorithms learn and think. Traditional forms of AI would require a human to carefully go through a list of patterns, rules and assumptions and manually build in or program that human experience into a computer. Modern forms of AI, however, allow computers to extract patterns and make inferences without a priori human assumptions. For example, if I wanted to teach the algorithm how to play the game of chess, I could simply explain the rules of chess and allow two AIs play against each other.

This paradigm shift is a completely new way to approach the design of learning algorithms. And, interestingly, this strategy has allowed modern AI systems to learn new or interesting information that may be previously unrecognized by even human experts. With video games, oftentimes we may think that the AI is intentionally losing, only to realize at the very end that the computer has come back and beat the human by a small but consistent margin every single time. For healthcare, the implication of course is that an AI may be allowed to discover patterns without the biases of flawed human assumptions or explicit programming thats really where the power lies.

And thats a core component of precision health healthcare informed by AI and machine learning. Typically, with advancements, though, there can also be downsides. Is there a downside to precision health?

I dont know if Id characterize it as the downside, but I will say that there is a lot of hype, which means that the expectations are oftentimes overinflated, and the inability to eventually meet those expectations and perhaps turn people away from the technology is something Im very aware of. Im obviously an advocate for this technology, but there are a lot of things we dont know. Its really in its infancy in terms of development and especially so in the field of medicine. The room for improvement is tremendous. So, we should acknowledge that. And we should acknowledge that the progress and potential, while its absolutely there, may be slow to realize.

UCI launched its Institute for Precision Health in February 2022. What most excites you about the Institute and what do you hope to achieve?

The IPH team is a strong collaborative team with diverse backgrounds. I think thats a key part to our unique approach to precision health and big data at UCI. At the same time, though the team is large, my role is very specific. In particular, my background is unique in that I both build modern AI algorithms on a daily basis and also practice as a board-certified radiologist. With this perspective, my hope is that Im able to bridge the gaps between technical and clinical experts to help accelerate translations in AI research for healthcare.

How unusual is it be a medical doctor and professor with the AI background?

Currently in 2022, this combination remains extraordinarily rare. As an illustrative example, Ive heard that Im the only physician teaching a technical deep learning class in a computer science department anywhere in the country. The course incorporates a hands-on curriculum building new AI algorithms each week with healthcare imaging data using the same libraries and tools developed by experts at Google, Facebook and Uber.

Before UCI, my real-life experience in AI started with research in the precision health AI field which eventually resulted in a startup company in the radiology deep-learning space. As part of the company, I work actively with our data science and engineering to innovate and translate the latest AI technologies into medical imaging diagnosis. In this capacity my experience with AI and machine learning comes from building state-of-the-art algorithms with industry-standard tools as well as regulatory clearance through the FDA, European CE-Mark and other international agencies. All of this experience is complementary to what you would normally expect of a medical doctor, I guess.

What led you to UCI?

When I was looking for full-time faculty positions, I wanted one that would allow me to continue pursuing hybrid clinical and AI work. Interestingly, that type of position really didnt exist three years ago. So, in large part what brought me to Irvine was as opportunity the UCI leadership saw for me in expanding AI and machine learning capacity across the healthcare community. More specifically, I was recruited here to build the AI center, an integral component of what is now the new Institute of Precision Health. While our team has grown tremendously, I was one of the first faculty in School of Medicine to dedicate my time and career to AI in healthcare. In this way, Ive been invested in the Institute for Precision Health from the very beginning.

The process of finding your dream job mustve been interesting.

If you had asked me four or five years ago where I most likely saw myself, I probably wouldve imagined myself working in industry. But, to put it simply, my single priority throughout has always been having access to resources that allow me to purse impactful work in healthcare AI. If that meant working at Google, I wouldve ended up at Google. But the reality is that here at UCI I was given a unique set of tools and resources that even the tech giants in industry could not match. As example, even Google with all the Google resources and talent in engineering and other data science does not have access to a hospital. By contrast, here at UCI I can take a tool built in the lab and turn it on the next day in a realistic clinical environment to see if it actually helps doctors do their day-to-day work. Theres the ability to connect with basic science researchers through clinicians who are world experts in treating some very specific diseases. And on top of all that, here at UCI I continue practicing medicine as a radiologist in the hospital one day a week.

When youre not practicing medicine or teaching, what does your job look like?

By design, I try to immerse myself in the technical details of new AI technology as much as possible. Working with students in the lab and writing software code is the highlight of day. Most of the time, youll find me with my engineering and data science team, both building algorithms and figuring out how best to plug those algorithms into clinical practice. You could say that my team are the boots on the ground to bring applications to real-life practice. And, of course, I spend a significant amount of my day with clinicians to discuss potential AI solutions for their daily problems. Almost invariably a conversation will start with, Peter, I have a problem if you could solve this problem, it would make my life a hundred times better.

How many are on your team?

We have about seven or eight full-time staff now at the Center for Artificial Intelligence, in addition to a large number of grant-sponsored students, trainees and postdocs. Additionally, I try to take on as many student volunteers as possible trainees who are looking just to get exposed to the field. Even without formal funding, there is a large community of individuals who just want to learn. Because of this, I think that the center has become a popular place around UCI.

What do you hope to tackle first now that IPH has launched?

Prior to the launch of IPH, a smaller group at UCI had been focused on precision health and omics. In parallel, my team was focused on AI and machine learning applied to precision health problems. In that regard, combining our expert backgrounds, we have some early projects looking at AI predictive analytics across multiple diagnostic modalities including electronic health record (EHR), radiology and omics data. including DNA, RNA, proteomics. This cross-disciplinary work truly embodies IPH and would quite frankly be impossible unless you had experts like those on our team to help guide you along the way. A few key priority areas of research specifically include ALS, dementia, gastric cancer and COVID.

Any last words about the future of precision health?

AI and precision health are exciting new areas of research, but for now Id urge everyone to stay grounded and be patient. There are a lot of unknowns and a lot to explore and understand, so a balanced perspective is needed to truly make strides translating these technologies in ways that ultimately will help researchers, clinicians and patients.

If you want to learn more about supporting this or other activities at UCI, please visit the Brilliant Future website athttps://brilliantfuture.uci.edu. Publicly launched on October 4, 2019, the Brilliant Future campaign aims to raise awareness and support for UCI. By engaging 75,000 alumni and garnering $2 billion in philanthropic investment, UCI seeks to reach new heights of excellence instudent success,health and wellness, research and more. UCI Health Affairs plays a vital role in the success of the campaign. Learn more by visitinghttps://brilliantfuture.uci.edu/uci-health-affairs/.

About UCI Institute for Precision Health: Founded in February 2022, the Institute for Precision Health (IPH) is a multifaceted, integrated ecosystem for collaboration that maximizes the collective knowledge of patient data sets and the power of computer algorithms, predictive modeling and AI. IPH marries UCIs powerhouse health sciences, engineering, machine learning, artificial intelligence, clinical genomics and data science capabilities to deliver the most effective health and wellness strategy for each individual person and, in doing so, confronts the linked challenges of health equity and the high cost of care. IPH is part of UCI Health Affairs, and is co-directed by Tom Andriola, vice chancellor for information, technology and data, and Leslie Thompson, Donald Bren Professor of psychiatry & human behavior and neurobiology & behavior. IPH is a comprised of seven areas: SMART(statistics, machine learning-artificial intelligence), A2IR(applied artificial intelligence research), A3(applied analytics and artificial intelligence), Precision Omics(fosters translation of genomic, proteomic, and metabolomic research findings into clinical applications), Collaboratory for Health & Wellness(providestheecosystem that fosters collaboration across disciplines through the integration of health-related data sources), Deployable Equity(engagescommunity stakeholders and health-equitygroupsto create solutionsthat narrow the disparities gap in the health and wellbeing of underserved and at-risk populations.) and Education and Training (brings data-centric education to students and healthcare practitioners so they can practice at the top of their licenses).

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Precision health perspectives - UCI News

Artificial Intelligence was an emerging technology a few years back, but now it has become an integral part of many industries, including the business world. With the adoption of Artificial Intelligence in the business world, the operation cost reduces, efficiency increases, and it further improves the customer experience. The end result is visible in the form of growth in the businesss revenue.

It plays a key role in automating monotonous tasks that save manual labour and save both time and money for the business. There is a direct connection between improved business productivity and operational efficiency. When implemented correctly, one can reap many benefits from Artificial Intelligence. Let us look at ways in which Artificial Intelligence can primarily improve business operations.

Customer Relationship Management

Irrespective of their business niche, a business, including logistic businesses like Ninja Van, often have to deal with customer relationship management; it is obvious because, of course, the business will be selling the products and services to a customer. The market is flooded with plenty of CRM tools. However, the fact is that even CRM platforms require heavy human intervention.

By integrating Artificial Intelligence into the CRM platform, it is now possible to create an auto-correcting and self-updating system. It helps your business to stay at the top of customer relationship management.

Intelligent Services

In a service-based business, Artificial Intelligence can be used to deliver intelligent service. It is especially true for the servitisation business model. This business model takes into account the customer data through Artificial Intelligence and helps the business craft the services and products that prove valuable to the customer.

A clear example of the usage of Artificial Intelligence in providing intelligent service can be seen on the Netflix recommendation engine. Based on what customers have watched or enjoyed, Netflix recommends similar movies and television shows for the customers that it knows the customer will enjoy.

In short, Artificial Intelligence in the service-based industry can keep both the company and the customer happy.

Improved Customer Experience

From the facts discussed above, it is crystal clear that Artificial Intelligence can play a pivotal role in improving the customer experience by delivering a highly personalised customer experience. For instance, Shopee Xpress ensures a good customer experience by allowing its customer to stay up to date regarding their shipment by allowing them to track it.

Artificial Intelligence can consider the customers various touch points and analyse what is driving the customer behaviour. With this important data and insight, the business can clearly see what else can be done to improve the customer experience.

In addition, Artificial Intelligence can further monitor customer sentiment and predict brand perception. It eliminates the guesswork required to create a positive brand image. It considers different parameters to forecast and is extremely helpful in creating a positive brand image.

Helps In Accounting

All businesses need to deal with accounting work. When accounting is done manually, there are chances of getting errors. This manual error can be eliminated with Artificial Intelligence. It can be used for performing repetitive actions like categorising different transactions and even recording the data. It can also create automatic invoices for different transactions.

Businesses can also employ Artificial Intelligence for even complex tasks like managing payroll. Automated payroll runs on cause and effect and may not always create the desired result. On the other hand, Artificial Intelligence is smart enough to analyse the data, learn from mistakes and devise strategic solutions. This helps in the efficient management of payroll.

Operational Data Analysis

Data plays a key role in any business. However, raw data is seldom of any use. A proper analysis of the raw data is necessary to create relevant insights. This insight, in turn, is valuable to improve the operation efficiency of the business.

Artificial Intelligence can also work toward data synthesis and help the human decision-maker make the right decision for the business. Other benefits of using Artificial Intelligence in operational data analysis include better inventory schemes and making the business resilient.

Artificial Intelligence has repeatedly proved that it can optimise internal business operations. However, its benefits go well beyond this. It can enhance the existing features of products and services, make better decisions, work on new marketing strategies, and further help the business pursue new markets.

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The role of artificial intelligence in improving business operations - Backend News

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Christchurch resident Kate Singleton says the way she shops has changed.

Christchurch resident Kate Singleton has been using artificial intelligence technology to change the way she shops, without even realising it.

She has started using Christchurch business MenuAid, an online recipe subscription service that sends customers meal ideas, and a shopping list of ingredients, for $4 a week.

Singleton said the app had completely changed the way her family cooked but she had no idea it was powered by artificial intelligence (AI).

It also helps avoid food wastage and cuts down on the cost of my weekly shop, Singleton said.

READ MORE:* Meal-planning tech entrepreneurs aiming to displace expensive meal kit services* NZ start-up uses smart tech to take on meal kit giants and dinner fatigue with MenuAid* Christchurch start-up wants to become the Edmonds Cookbook for the digital generation

Singleton said a major problem with many of the meal delivery services was that the spices and ingredients used for one meal could then sit untouched at the back of the pantry.

MenuAid uses its AI to track what ingredients a customer should have in their pantry and suggests meals that make the most of what is on offer.

Melody Tia-Peni used to spend more than $400 on a weekly shop for her household of two teenagers and two grandchildren.

But MenuAid had brought that down to between $200 and $250 a week. Much of the savings came from avoiding food wastage, she said.

Every individual, and every familys palate, is different. So we had to create a recommendation engine that can very quickly adapt to a range of tastes. To do that we have built an AI which is getting smarter and smarter, MenuAid founder Toby Skilton said.

ALDEN WILLIAMS/Stuff

MenuAid co-founders Elise Hilliam and Toby Skilton have created a meal subscription service powered by artificial intelligence technology.

When users sign up to MenuAid, the system records a range of food preferences to kick-start the recommendation engine.

As users cook and review recipes, the system collects data to create more accurate recommendations.

It records things like whether the person prefers quick and easy meals over longer cook times, whether they prefer pork or chicken. We also have a personal clicking history of the meals they were most interested in. We put this data together and create an in-depth profile of a users preferences.

The data is not only used to recommend recipes but also to help with the act of shopping.

When a user has finalised their recipes for the week, they get a shopping list which they can order online through Countdown delivery or shop for themselves.

The MenuAid system also collects information about the way a user shops, whether they prefer health food products, or cheaper brands, or a particular produce or protein, and can then recommend recipes based on this information.

The system does a first pass of the recommendations but the user can always change things. The cool thing is if a user does change, then the system remembers that and takes the preference on board for next time.

It is like having an AI personal shopping assistant, Skilton said.

The cost of living at the moment is insane, everyone has been feeling it. It has been really amazing to hear the stories from our customers and to know we are making a difference in their lives, he said.

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How some shoppers are using artificial intelligence to halve the cost of their groceries - Stuff

For decades, scientists have been giddy and citizens have been fearful of the power of computers. In 1965 Herbert Simon, a Nobel laureate in economics and also a winner of the Turing Award (considered The Nobel Prize of computing), predicted that machines will be capable, within 20 years, of doing any work a man can do. His misplaced faith in computers is hardly unique. Sixty-seven years later, we are still waiting for computers to become our slaves and masters.

Businesses have spent hundreds of billions of dollars on AI moonshots that have crashed and burned. IBMs Dr. Watson was supposed to revolutionize health care and eradicate cancer. Eight years later, after burning through $15 billion with no demonstrable successes, IBM fired Dr. Watson.

In 2016 Turing Award Winner Geoffrey Hinton advised that We should stop training radiologists now. Its just completely obvious that within five years, deep learning is going to do better than radiologists. Six years later, the number of radiologists has gone up, not down. Researchers have spent billions of dollars working on thousands of radiology image-recognition algorithms that are not as good as human radiologists.

(Iaroshenko Maryna/Shutterstock)

What about those self-driving vehicles, promised by many including Elon Musk in his 2016 boast that I really consider autonomous driving a solved problem. I think we are probably less than two years away. Six years later, the most advanced self-driving vehicles are arguably Waymos in San Francisco, which only operate between 10 p.m. and 6 a.m. on the least crowded roads and still have accidents and cause traffic tie-ups. They are a long way from successfully operating in downtown traffic during the middle of the day at a required 99.9999% level of proficiency.

The list goes on. Zillows house-flipping misadventure lost billions of dollars trying to revolutionize home-buying before they shuttered it. Carvanas car-flipping gambit still loses billions.

We have argued for years that we should be developing AI that makes people more productive instead of trying to replace people. Computers have wondrous memories, make calculations that are lightning-fast and error-free, and are tireless, but humans have the real-world experience, common sense, wisdom and critical thinking skills that computers lack. Together, they can do more than either could do on their own.

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Effective augmentation appears to be finally happening with medical images. A large-scale study just published in Lancet Digital Health is the first to directly compare AI cancer screening when used alone or to assist humans. The software comes from a German startup, Vara, whose AI is already used in more than 25% of Germanys breast cancer screening centers.

Researchers from Vara, Essen University and the Memorial Sloan Kettering Cancer Center trained the algorithm on more than 367,000 mammograms, and then tested it on 82,851 mammograms that had been held back for that purpose.

In the first strategy, the algorithm was used alone to analyze the 82,851 mammograms. In the second strategy, the algorithm separated the mammograms into three groups: clearly cancer, clearly no cancer, and uncertain. The uncertain mammograms were then sent to board-certified radiologists who were given no information about the AI diagnosis.

Doctors and AI working together turned out to be better than either working alone. The AI pre-screening reduced the number of images the doctors examined by 37% while lowering the false-positive and false-negative rates by about a third compared to AI alone and by 14%-20% compared to doctors alone. Less work and better results!

As machine learning improves, the AI analysis of X-rays will no doubt become more efficient and accurate. There will come a time when AI can be trusted to work alone. However, that time is likely to be decades in the future and attempts to jump directly to that point are dangerous.

We are optimistic that the productivity of many workers can be improved by similar augmentation strategies not to mention the fact that many of the tasks that computers excel at are dreadful drudgery; e.g., legal research, inventory control and statistical calculations. But far too many attempts to replace humans entirely have not only been an enormous waste of resources but have also undermined the credibility of AI research. The last thing we need is another AI winter where funding dries up, resources are diverted and the tremendous potential of these technologies are put on hold. We are optimistic that the accumulating failures of moonshots and successes of augmentation strategies will change the way that we think about AI.

Funk is an independent technology consultant who previously taught at National University of Singapore, Hitotsubashi and Kobe Universities in Japan, and Penn State, where he taught courses on the economics of new technologies. Smith is the author of The AI Delusion and co-author (with Jay Cordes) of The 9 Pitfalls of Data Science and The Phantom Pattern Problem.

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The right and wrong way to use artificial intelligence - New York Daily News

Rishi Sunak has said that as prime minister he would encourage the use of artificial intelligence (AI) technology in the classroom to reduce the workload on teachers.

The promise to champion new technology in schools forms part of a package which he said would radically reform education to put British kids ahead.

The former chancellor, who is vying with Liz Truss for the Conservative leadership, hailed school reforms ushered in by Michael Gove as one of the partys greatest achievements of its 12 years in power, and said he would build on them by opening more free schools in areas with the poorest attainment.

He promised a transformation in post-16 education to drive up the prestige of apprenticeships and vocational courses and shut down university degrees with a poor record of getting students into well-paying jobs.

Mr Sunak said he would create a Russell Group of world-class technical institutions mirroring the elite group of academic universities to give apprenticeship and T-level students confidence that they are receiving the best available education in their chosen fields. Regius professorships would be established to identify and celebrate the best teaching.

And he said he would bear down on university degrees that saddle students with debt without improving their life chances.

Drawing on Institute for Fiscal Studies research suggesting that 20 per cent of students are left with lower earning potential as a result of going to university, Mr Sunak said he would take a robust approach to degrees with poor outcomes.

Courses will be judged on factors including drop-out rates, numbers moving on to graduate jobs and salary thresholds - with key exceptions for courses with high social value like nursing.

Aides denied this would mean favouring science courses over the arts, pointing to research showing that creative arts degrees at some institutions deliver better returns for students than economics degrees elsewhere.

The former chancellor said he would harness AI and digital teaching resources to reduce teacher workload and inspire pupils.

The Department for Education would receive a new mandate to explore how more digital technology could be used in schools to deliver hybrid learning to supplement teachers.

Mr Sunak said: A good education is the closest thing we have to a silver bullet when it comes to making peoples lives better.

These proposals represent a significant stride towards parity of esteem between vocational and academic education. And they will take a tougher approach to university degrees that saddle students with debt, without improving their earning potential.

I will also take bold, practical steps to build on the successful Conservative education reforms of the past decade by harnessing technology and improving the quality of teaching in underperforming areas.

Every child deserves a world-class education and, if I become prime minister, I will make it my mission from day one to ensure thats what they get.

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Sunak promises artificial intelligence in schools to cut teachers workloads - The Independent

Online casinos are increasing the use of artificial intelligence to enhance a players gambling experience. With internet use rising over the past decade, the online casino market has more than doubled over that period. It is estimated that by 2024, the numbers will have gone over 65 million in the UK alone.

It may seem obvious why the online market has such high growth rates, especially considering the events that occurred in the last couple of years and how they pushed people to use the internet for many services, including retail and banking. In this editorial, we examine the effects of artificial intelligences continued advancement in the casino industry, with a focus on the real money slot game on amazon slots.

Several factors contribute to the online casino market turning into a billion-pound industry. For instance, digital wallets such as PayPal have assisted casinos in appearing more accessible to people who want instant transactions. Similarly, daily and weekly bonuses and promotions have increased registration numbers on online casino sites.

Another example is the features found in the slot games that offer free spins daily and help increase players odds of striking the big jackpots, winning cash prizes, live casino bonuses, and free sportsbook wagers. And, if punters dont get it right the first few times, they have an incentive to keep trying.

So, attributes such as electronic payment options, daily bonuses, and promotions all encourage new and existing players to keep coming back for more. Thus, increasing winning odds for returning players and the industrys market value goes up all the same.

The introduction of AI technology has remarkably affected the online casino industry by transforming it into what it is these days. How is that, you ask? Over the past few years, casino operators and gaming developers have had more access to user information, which has aided them in creating and improving games best suited for their customers.

The application of AI in gathering user data on new and returning players has assisted operators and developers in keeping fresh content that maintains relevance while creating targeted marketing campaigns. AI helps determine which games players engage with the most, how much traffic a casino site receives, and how much wagering takes place on games and sporting events. Advanced bots in gambling provide higher quality customer service in online gambling.

The good news for casino providers is that AI could save a lot on personnel costs. The bad news for those employees is that they would end up losing work to robots, which means an entire industry would take a hit. The overall positive thing about it all, though, is that people are innovative by nature. Perhaps the closing of one old industry might drive an opening for another.

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How Artificial Intelligence Is Changing The Odds In Online Casino - Intelligent Living

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Dublin, Aug. 05, 2022 (GLOBE NEWSWIRE) -- The "Artificial Intelligence of Things Solutions by AIoT Market Applications and Services in and Industry Verticals 2022 - 2027" report has been added to ResearchAndMarkets.com's offering.

This AIoT market report provides an analysis of technologies, leading companies and solutions. The report also provides quantitative analysis including market sizing and forecasts for AIoT infrastructure, services, and specific solutions for the period 2022 through 2027. The report also provides an assessment of the impact of 5G upon AIoT (and vice versa) as well as blockchain and specific solutions such as Data as a Service, Decisions as a Service, and the market for AIoT in smart cities.

While it is no secret that AI is rapidly becoming integrated into many aspects of ICT, many do not understand the full extent of how it will transform communications, applications, content, and commerce. For example, the use of AI for decision-making in IoT and data analytics will be crucial for efficient and effective smart city solutions in terms of decision-making.

The convergence of AI and Internet of Things (IoT) technologies and solutions (AIoT) is leading to "thinking" networks and systems that are becoming increasingly more capable of solving a wide range of problems across a diverse number of industry verticals. AI adds value to IoT through machine learning and improved decision-making. IoT adds value to AI through connectivity, signaling, and data exchange.

AIoT is just beginning to become part of the ICT lexicon as the possibilities for the former adding value to the latter are only limited by the imagination. With AIoT, AI is embedded into infrastructure components, such as programs, chipsets and edge computing, all interconnected with IoT networks.APIs are then used to extend interoperability between components at the device level, software level and platform level. These units will focus primarily on optimizing system and network operations as well as extracting value from data.

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While early AIoT solutions are rather monolithic, it is anticipated that AIoT integration within businesses and industries will ultimately lead to more sophisticated and valuable inter-business and cross-industry solutions. These solutions will focus primarily upon optimizing system and network operations as well as extracting value from industry data through dramatically improved analytics and decision-making processes.

Industry adoption for AIoT is gaining momentum. By way of example, Advantech partnered with Momenta Ventures to launch the AIoT Ecosystem Fund, a venture capital fund with a target of $50 million USD and a focus on the digital industry. KC Liu, CEO of Advantech, stated: "Advantech is committed to enabling an intelligent planet. This starts at the industrial edge with early innovators in energy, manufacturing, smart spaces and supply chain management."

The company launched Advantech Industrial Wireless solutions with Qualcomm, NXP, DEKRA, and E Ink. "We provide AIW industrial grade wireless modules and wireless design-in services to embedded customers. This one-stop shopping service helps customers acquire leading wireless enabled AIoT products and reduce their time to market," said Andy Lin, Advantech Senior ProductManager.

Many industry verticals will be transformed through AI integration with enterprise, industrial, and consumer product and service systems. It is destined to become an integral component of business operations including supply chains, sales and marketing processes, product and service delivery, and support models.

We see AIoT evolving to become more commonplace as a standard feature from big analytics companies in terms of digital transformation for the connected enterprise. This will be realized in infrastructure, software, andSaaS managed service offerings. Recent years have witnessed rapid growth for IoT data-as-a-service offerings to become AI-enabled decisions-as-a-service-solutions, customized on a per industry and company basis. Certain data-driven verticals such as the utility and energy service industries will lead the way.

As IoT networks proliferate throughout every major industry vertical, there will be an increasingly large amount of unstructured machine data. The growing amount of human-oriented and machine-generated data will drive substantial opportunities for AI support of unstructured data analytics solutions. Data generated from IoT-supported systems will become extremely valuable, both for internal corporate needs as well as for many customer-facing functions such as product life-cycle management.

The use of AI for decision-making in IoT and data analytics will be crucial for efficient and effective decision-making, especially in the area of streaming data and real-time analytics associated with edge computing networks. Real-time data will be a key value proposition for all use cases, segments, and solutions. The ability to capture streaming data, determine valuable attributes, and make decisions in real-time will add an entirely new dimension to service logic.

In many cases, the data itself, and actionable information will be the service. AIoT infrastructure and services will, therefore, be leveraged to achieve more efficient IoT operations, improve human-machine interactions, and enhance data management and analytics, creating a foundation for IoT Data as a Service (IoTDaaS) and AI-based Decisions as a Service.

The fastest-growing 5G AIoT applications involve private networks. Accordingly, the 5GNR market for private wireless in industrial automation will reach $5.21B by 2027. Some of the largest market opportunities will be AIoT market IoTDaaS solutions. We see machine learning in edge computing as the key to realizing the full potential of IoT analytics.

Select Report Findings:

The global AIoT market will reach $83.6Billion by 2027, growing at 39.1% CAGR

The global market for IoT data as service solutions will reach $9.13B USD by 2027

The AI-enabled edge device market will be the fastest-growing segment within the AIoT

AIoT automates data processing systems, converting raw IoT data into useful information

Today's AIoT solutions are the precursor to next-generation AI Decision as a Service (AIDaaS)

AIoT solutions improve operational effectiveness and the value of machine data by up to 28% by 2027

Key Topics Covered:

1.0 Executive Summary

2.0 Introduction2.1 Defining AIoT2.2 AI in IoT vs. AIoT2.3 Artificial General Intelligence2.4 IoT Network and Functional Structure2.5 Ambient Intelligence and Smart Lifestyles2.6 Economic and Social Impact2.7 Enterprise Adoption and Investment2.8 Market Drivers and Opportunities2.9 Market Restraints and Challenges2.10 AIoT Value Chain2.10.1 Device Manufacturers2.10.2 Equipment Manufacturers2.10.3 Platform Providers2.10.4 Software and Service Providers2.10.5 User Communities

3.0 AIoT Technology and Market3.1 AIoT Market3.1.1 Equipment and Component3.1.2 Cloud Equipment and Deployment3.1.3 3D Sensing Technology3.1.4 Software and Data Analytics3.1.5 AIoT Platforms3.1.6 Deployment and Services3.2 AIoT Sub-Markets3.2.1 Supporting Device and Connected Objects3.2.2 IoT Data as a Service3.2.3 AI Decisions as a Service3.2.4 APIs and Interoperability3.2.5 Smart Objects3.2.6 Smart City Considerations3.2.7 Industrial Transformation3.2.8 Cognitive Computing and Computer Vision3.2.9 Consumer Appliances3.2.10 Domain-Specific Network Considerations3.2.11 3D Sensing Applications3.2.12 Predictive 3D Design3.3 AIoT Supporting Technologies3.3.1 Cognitive Computing3.3.2 Computer Vision3.3.3 Machine Learning Capabilities and APIs3.3.4 Neural Networks3.3.5 Context-Aware Processing3.4 AIoT Enabling Technologies and Solutions3.4.1 Edge Computing3.4.2 Blockchain Networks3.4.3 Cloud Technologies3.4.4 5G Technologies3.4.5 Digital Twin Technology and Solutions3.4.6 Smart Machines3.4.7 Cloud Robotics3.4.8 Predictive Analytics and Real-Time Processing3.4.8.1 All-Flash Array3.4.8.2 Real-Time Operating Systems3.4.9 Post Event Processing3.4.10 Haptic Technology

4.0 AIoT Applications Analysis4.1 Device Accessibility and Security4.2 Gesture Control and Facial Recognition4.3 Home Automation4.4 Wearable Device4.5 Fleet Management4.6 Intelligent Robots4.7 Augmented Reality Market4.8 Drone Traffic Monitoring4.9 Real-time Public Safety4.10 Yield Monitoring and Soil Monitoring Market4.11 HCM Operation

5.0 Analysis of Important AIoT Companies5.1 Sharp5.2 SAS5.3 DT425.4 Chania Tech Giants: Baidu, Alibaba, and Tencent5.4.1 Baidu5.4.2 Alibaba5.4.3 Tencent5.5 Xiaomi Technology5.6 NVidia5.7 Intel Corporation5.8 Qualcomm5.9 Innodisk5.10 Gopher Protocol5.11 Micron Technology5.12 ShiftPixy5.13 Uptake5.14 C3 IoT5.15 Alluvium5.16 Arundo Analytics5.17 Canvass Analytics5.18 Falkonry5.19 Interactor5.20 Google5.21 Cisco5.22 IBM Corp.5.23 Microsoft Corp.5.24 Apple Inc.5.25 Salesforce Inc.5.26 Infineon Technologies AG5.27 Amazon Inc.5.28 AB Electrolux5.29 ABB Ltd.5.30 AIBrian Inc.5.31 Analog Devices5.32 ARM Limited5.33 Atmel Corporation5.34 Ayla Networks Inc.5.35 Brighterion Inc.5.36 Buddy5.37 CloudMinds5.38 Cumulocity GmBH5.39 Cypress Semiconductor Corp5.40 Digital Reasoning Systems Inc.5.41 Echelon Corporation5.42 Enea AB5.43 Express Logic Inc.5.44 Facebook Inc.5.45 Fujitsu Ltd.5.46 Gemalto N.V.5.47 General Electric5.48 General Vision Inc.5.49 Graphcore5.50 H2O.ai5.51 Haier Group Corporation5.52 Helium Systems5.53 Hewlett Packard Enterprise5.54 Huawei Technologies5.55 Siemens AG5.56 SK Telecom5.57 SoftBank Robotics5.58 SpaceX5.59 SparkCognition5.60 STMicroelectronics5.61 Symantec Corporation5.62 Tellmeplus5.63 Tend.ai5.64 Tesla5.65 Texas Instruments5.66 Thethings.io5.67 Veros Systems5.68 Whirlpool Corporation5.69 Wind River Systems5.70 Juniper Networks5.71 Nokia Corporation5.72 Oracle Corporation5.73 PTC Corporation5.74 Losant IoT5.75 Robert Bosch GmbH5.76 Pepper5.77 Terminus5.78 Tuya Smart

6.0 AIoT Market Analysis and Forecasts 2022 - 20276.1 Global AIoT Market Outlook and Forecasts6.1.1 Aggregate AIoT Market 2022 - 20276.1.2 AIoT Market by Infrastructure and Services 2022 - 20276.1.3 AIoT Market by AI Technology 2022 - 20276.1.4 AIoT Market by Application 2022 - 20276.1.5 AIoT in Consumer, Enterprise, Industrial, and Government 2022 - 20276.1.6 AIoT Market in Cities, Suburbs, and Rural Areas 2022 - 20276.1.7 AIoT in Smart Cities 2022 - 20276.1.8 IoT Data as a Service Market 2022 - 20276.1.9 AI Decisions as a Service Market 2022 - 20276.1.10 Blockchain Support of AIoT 2022 - 20276.1.11 AIoT in 5G Networks 2022 - 20276.2 Regional AIoT Markets 2022 - 20276.3 AIoT Unit Deployment 2022 - 20276.3.1 Global AIoT Unit Deployment 2022 - 20276.3.2 AIoT Unit Deployment by Type 2022 - 20276.3.3 AIoT Unit Deployment by Region 2022 - 2027

7.0 Conclusions and Recommendations

For more information about this report visit https://www.researchandmarkets.com/r/if7nau

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Global Artificial Intelligence of Things Solutions Market Report 2022: AIoT Solutions Improve Operational Effectiveness and the Value of Machine Data...

image:Map detailing ant diversity centers in Africa, Madagascar and Mediterranean regions. view more

Credit: Kass et al., 2022, Science Advances

E. O. Wilson once referred to invertebrates as the little things that run the world, without whom the human species [wouldnt] last more than a few months. Although small, invertebrates have an outsized influence on their environments, pollinating plants, breaking down organic matter and speeding up nutrient cycling. And what they lack in stature, they make up for in diversity. With more than one million known species, insects alone vastly outnumber all other invertebrates and vertebrates combined.

Despite their importance and ubiquity, some of the most basic information about invertebrates, such as where theyre most diverse and how many of them there are, still remains a mystery. This is especially problematic for conservation scientists trying to stave off global insect declines; you cant conserve something if you dont know where to look for it.

In a new study published this Wednesday in the journal Science Advances, researchers used ants as a proxy to help close major knowledge gaps and hopefully begin reversing these declines. Working for more than a decade, researchers from institutions around the world stitched together nearly one-and-a-half million location records from research publications, online databases, museums and scientific field work. They used those records to help produce the largest global map of insect diversity ever created, which they hope will be used to direct future conservation efforts.

This is a massive undertaking for a group known to be a critical ecosystem engineer, said co-author Robert Guralnick, curator of biodiversity informatics at the Florida Museum of Natural History. It represents an enormous effort not only among all the co-authors but the many naturalists who have contributed knowledge about distributions of ants across the globe.

Creating a map large enough to account for the entirety of ant biodiversity presented several logistical challenges. All currently known ant species were included, which numbered at more than 14,000, and each one varied dramatically in the amount of data available.

The majority of the records used contained a description of the location where an ant was collected or spotted but did not always have the precise coordinates needed for mapping. Inferring the extent of an ants range from incomplete records required some clever data wrangling.

Co-author Kenneth Dudley, a research technician with the Okinawa Institute of Science and Technology built a computational workflow to estimate the coordinates from the available data, which also checked the data for errors. This allowed the researchers to make different range estimates for each species of ant depending on how much data was available. For species with less data, they constructed shapes surrounding the data points. For species with more data, the researchers predicted the distribution of each species using statistical models that they tuned to reduce as much noise as possible.

The researchers brought these estimates together to form a global map, divided into a grid of 20 km by 20 km squares, that showed an estimate of the number of ant species per square (called the species richness). They also created a map that showed the number of ant species with very small ranges per square (called the species rarity). In general, species with small ranges are particularly vulnerable to environmental changes.

However, there was another problem to overcomesampling bias.

Some areas of the world that we expected to be centers of diversity were not showing up on our map, but ants in these regions were not well-studied, explained co-first author Jamie Kass, a postdoctoral fellow at the Okinawa Institute of Science and Technology. Other areas were extremely well-sampled, for example parts of the USA and Europe, and this difference in sampling can impact our estimates of global diversity.

So, the researchers utilized machine learning to predict how their diversity would change if they sampled all areas around the world equally, and in doing so, identified areas where they estimate many unknown, unsampled species exist.

This gives us a kind of treasure map, which can guide us to where we should explore next and look for new species with restricted ranges, said senior author Evan Economo, a professor at the Okinawa Institute of Science and Technology.

When the researchers compared the rarity and richness of ant distributions to the comparatively well-studied amphibians, birds, mammals and reptiles, they found that ants were about as different from these vertebrate groups as the vertebrate groups were from each other.

This was unexpected given that ants are evolutionarily highly distant from vertebrates, and it suggests that priority areas for vertebrate diversity may also have a high diversity of invertebrate species. The authors caution, however, that ant biodiversity patterns have unique features. For example, the Mediterranean and East Asia show up as diversity centers for ants more than the vertebrates.

Finally, the researchers looked at how well-protected these areas of high ant diversity are. They found that it was a low percentageonly 15% of the top 10% of ant rarity centers had some sort of legal protection, such as a national park or reserve, which is less than existing protection for vertebrates.

Clearly, we have a lot of work to do to protect these critical areas, Economo concluded.

The global distribution of known and undiscovered ant biodiversity

3-Aug-2022

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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Researchers use artificial intelligence to create a treasure map of undiscovered ant species - EurekAlert