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Artificial intelligence is growing rapidly in China, but there are still wide gaps in industrial application - SupChina

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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Women are more sceptical than men about some uses of artificial intelligence (AI). This is the case concerning use of driverless cars and using AI to find false information on social media, according to a new analysis of US data made by Pew Research Center.

34% of women are unsure about whether social media algorithms to find false information are a good or bad idea, compared with 26% of men. When it comes to the use of face recognition by police, 31% of women are not certain whether it is a good or bad idea, compared with 22% of men.

Women are more likely to support the inclusion of a wider variety of groups in AI design. 67% of women say its extremely or very important for social media companies to include people of different genders when designing social media algorithms to find false information, compared with 58% of men. Women are also more likely to say it is important that different racial and ethnic groups are included in the same AI design process (71% vs. 63%).

Additionally, women are more doubtful than men that it is possible to design AI computer programs that can consistently make fair decisions in complex situations. Only around two-in-ten women (22%) think it is possible to design AI programs that can consistently make fair decisions, while a larger share of men (38%) say the same. A plurality of women (46%) say they are not sure whether this is possible, compared with 35% of men.

Overall, women in the U.S. are lesslikely than men to say that technology has had a mostly positive effect on society (42% vs. 54%) and morelikely to say technology has had equally positive and negative impacts (45% vs. 37%). In addition, women are less likely than men to say they feel more excited than concerned about the increased use of AI computer programs in daily life (13% vs. 22%).

Gender remains a factor in views about AI and technologys impact when accounting for other variables, such as respondents political partisanship, education and race and ethnicity.

The analysis says women are consistently more likely than men to express concern about computer programs executing tasks. 43% of women say they would be very or somewhat concerned if AI programs could diagnose medical problems, while 27% of men say the same.

In addition to gender differences about AI in general, women and men express different attitudes about autonomous cars, specifically, the analysis says

37% of men say driverless cars are a good idea for society, while 17% of women say the same. Women, are somewhat more likely than men to say they are not sure if the widespread use of driverless vehicles is a good or bad idea (32% vs. 25%).

46% of men say they would definitely or probably personally want to ride in a driverless passenger vehicle if given the opportunity, compared with 27% of women. 54% of women say they would not feel comfortable sharing the road with a driverless passenger vehicle if their use becomes widespread. Only 35% of men say the same.

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Our mission is to provide top and unbiased information for all professionals and to make sure that women get their fair share of voice in the news and in the spotlight!

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Among the many drivers of the tech ecosystems rapid growth, artificial intelligence (AI) and its subdomains are at the fore. Described by Gartner as the application of advanced analysis and logic-based techniques to simulate human intelligence, AI is an all-inclusive system with numerous use cases for individuals and enterprises across industries.

There are many ways of leveraging AI to support, automate and augment human tasks, as seen by the range of solutions available today. These offerings promise to simplify complex tasks with speed and accuracy, and to spur new applications that were impractical or possible previously. Some question whether the technology will be used for good or perhaps become more effective than humans for certain business use cases, but its prevalence and popularity cannot be doubted.

AI software can be defined in several ways. First, a lean description would consider it to be software that is capable of simulating intelligent human behavior. However, a broader perspective sees it as a computer application that learns data patterns and insights to meet specific customer pain points intelligently.

The AI software market includes not just technologies with built-in AI processes, but also the platforms that allow developers to build AI systems from scratch. This could range from chatbots to deep and machine learning software and other platforms with cognitive computing capabilities.

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To get a sense of the scope, AI encompasses the following:

These capabilities are leveraged to build AI software for different use cases, the top of which are knowledge management, virtual assistance and autonomous vehicles. With the large volumes of data that enterprises must comb through to meet customer demands, theres an increased need for faster and more accurate software solutions.

As expected, the rise in enterprise-level adoption of AI has led to accelerated market growth of the global AI software market. Gartner places the growth at an estimated $62.5 billion in 2022 a 21.3% increase on its value in 2021. By 2025, IDC projects this market to reach $549.9 billion.

Whether it powers surgical bots in healthcare, detects fraud in financial transactions, strengthens driver assistance technology in the automotive industry or personalizes learning content for students, the overarching purpose of AI solutions can be grouped into four broad functional categories, including:

The automation function of AI applications meets AIs primary objective to minimize human intervention in executing tasks, whether mundane and repetitive or complex and challenging. By collecting and interpreting volumes of data fed into it, an AI solution can be leveraged to determine the next steps in a process and execute it seamlessly. It does this by leveraging the capabilities of ML algorithms to create a knowledge base of structured and unstructured data.

Process automation remains a top enterprise concern, with one survey exhibiting that 80% of companies expect to adopt intelligent automation in 2027.

A core function of AI solutions, especially for enterprises, is to create knowledge bases of structured and unstructured data and then analyze and interpret such data before making predictions and recommendations from its findings. This is called AI analytics and it uses machine learning to study data and draw patterns.

Whether the analytic tools are predictive, prescriptive, augmented, or even descriptive, AI is at the center of determining how the data is prepared, discovering new insights and patterns and predicting business outcomes. Enterprises are also turning to AI for improved data quality.

Building a relationship has become the holy grail of customer acquisition and retention. A study from McKinsey shows that one sure way to do this is through personalization and engagement. AI technologies allow enterprises to make personalized offers to customers and predict and solve their concerns in real-time. This function manifests in programs like conversational chatbots and product recommendations generated from learned customer behavior.

Many organizations are still getting up to speed with the technology. Gartner reports that 63% of digital marketers struggle to maximize personalization technology. Their survey of 350 marketing executives revealed that only 17% are actively using AI and ML solutions across the board, although 83% believe in its potency.

Along with greater automation of traditional processes, AI enables new services and capabilities that were not previously feasible. From driverless vehicles and natural language services for consumers to medical breakthroughs that could only have been imagined previously, AI is becoming the base of new products and markets that will continue to unfold.

Also read: Creating responsible AI products using human oversight

AI software solutions include general platforms for supporting a range of applications and products for more narrow, industry-specific use cases. We include a sampling of both in the following representative list. With 56% of organizations adopting AI for at least one business function, there are many options on the market today.

Below are ten examples of AI software solutions available in 2022.

Googles dominant cloud offering includes assorted tools to support developer, data science and infrastructure use cases. Several speech and language translation tools, vision, audio and video tools and deep and machine earning capabilities bring AI functionality to skilled technology practitioners and mass consumer markets. Google was named a leader in Gartners Magic Quadrant for Cloud AI Developer Services in 2022.

Like Google, IBM offers a platform for building and training AI software. The IBM Watson Studio provides a multicloud architecture for developers, data scientists and analysts to build, run and manage AI models collaboratively. With capabilities ranging from AutoAI to explainable AI, DL, model drift, modelops and model risk management, the studio gives subject-matter experts the tools they need to either gather and prepare data or create and train AI models.

It also allows these professionals the flexibility to deploy AI models on either public or private cloud (IBM Cloud Pak, Microsoft Azure, Google Cloud, or Amazon Web Services) and on-premises. IT teams can open source these models as they build them with embedded Waston tools like the Natural Language Classifier. Its hybrid environment may also provide developers with more data access and agility.

Named a leader in Gartners Magic Quadrant for CRM Customer Engagement Center thirteen times in a row and the #1 CRM solution for eight consecutive years by the International Data Corporation (IDC), Salesforce provides an advanced kit of sales, marketing and customer experience tools. Salesforce Einstein is an AI product that helps companies identify patterns in customer data.

This platform has a set of built-in AI technologies supporting the Einstein bots, prediction builder, forecasting, commerce cloud Einstein, service cloud Einstein, marketing cloud Einstein and other functions. Users and developers of new and existing cloud applications can also deploy the platforms predictive and suggestive capabilities into their models. For example, at Salesforce Einsteins launch in 2016, John Ball, general manager at Einstein, revealed that by creating Einstein, the company enables sales professionals to find better prospects and close more deals through predictive lead scoring and automatic data capture to convert leads into opportunities and opportunities into deals.

Oculeus provides an industry-specific solution. For service providers, network operators and enterprises in the telecom industry that need to protect and defend their communication infrastructure against cyber threats, Oculeus offers a portfolio of software-based solutions that can help them better manage network operations. According to founder and CEO Arnd Baranowski, Oculeus uses AI and automation to learn about an enterprises regular communications traffic and continually monitor it for exceptions to a baseline of expected communications activities. With its AI-driven technologies, suspicious traffic can be identified, investigated and blocked within milliseconds. This is done before any significant financial damage is caused to the enterprise and protects the brand reputation of the telecoms service provider.

The Communications Fraud Control Association (CFCA)s 2021 survey of international telecommunication fraud loss discovered losses amounting to over $39.89 billion, a 28% increase in losses over the previous year. Similarly, network security and operators are experiencing more fraud threats and attacks.

Among other things, these insights amplify the need for enterprises to switch to a proactive defense approach that outwits adversaries, and this what Oculeus claims to provide with its AI-powered telecoms fraud protection solutions. In Baranowskis words, Oculeus AI-driven approach to telecoms fraud protection does not only stop fraudulent telecommunications traffic before any significant financial damage is caused but also includes extensive automation tools that weed out threats thoroughly.

Edsoma represents another narrow use case. Its AI-based reading application software features real-time, exclusive voice identification and recognition technology designed to uncover the strengths and weaknesses in childrens reading. This follow-along technology identifies users spoken words and speaking speed to determine if they are saying the words correctly. A correction program helps put them back on track if they mispronounce something.

As Edsoma founder and CEO Kyle Wallgren explained, once the electronic book is read, the childs voice is transcribed in real-time by the automated speech recognition (ASR) system and immediate results are provided, including pronunciation assessment, phonetics, timing and other facets. These metrics are compiled to help teachers and parents make informed decision.

This technology aims to improve childrens oral reading fluency skills and provide them the necessary support to inculcate a healthy reading culture. Edsoma seeks to establish a share of the $127 billion global edtech market. By leveraging real-time data to provide real-time literacy, Edsoma looks to provide future-focused learning powered by AI.

Appen has been one of the early leaders as a source for data required throughout the development lifecycle of AI products. This platform provides and improves image and video data, language processing, text and even alphanumeric data.

It follows four steps in preparing data for AI processing: the first step is data sourcing which offers automatic access to over 250 pre-labeled datasets then data preparation, which provides data annotation, data labeling and knowledge graphs and ontology mapping.

The third stage supports model building and development needs with the help of partners like Amazon Web Services, Microsoft, Nvidia and Google Cloud AI. The final step combines a human evaluation and AI system benchmarking, giving developers an understanding of how their modes work.

Appen boasts a lingual database of more than 180 languages and a global skill force of over 1 million talents. Of its many features, its AI-assisted data annotation platform is the most popular.

Cognigy is a low-code conversational AI and automation platform recently named a leader in Gartners 2022 Magic Quadrant for Enterprise Conversational AI platforms. As the need for more excellent customer experience (CX) intensifies, more enterprises rely on conversational analytics solutions that dive deep into its customers text and voice data and discover insights that inform smarter decisions and automate processes.

This is why Cognigy automates natural communication among employees and customers on multimodal channels and in over 100 languages. In addition, its technology allows enterprises to set up AI-powered voice and chatbots that can address customer concerns with human-like accuracy.

Cognigy also has an analytics feature Cognigy Insights that provides enterprises with data-driven insights on the best ways to optimize their virtual agents and contact centers. In addition, the platform allows users to either deploy the technology on the cloud or on-premises. Particularly praised by Gartner for its customer references, flexibility and sustainability, this platform helps businesses create new service experiences for customers.

Synthesis AIs solution generates synthetic data that allows developers to create more capable and ethical AI models. Engineers can source several well-labeled, photorealistic images and videos in deploying its models on this platform. These images and videos come perfectly labeled with labels ranging from depth maps, surface normals, segmentation maps, and even 2D/3D landmarks.

Virtual product prototyping and the chance to build more ethical AI with expanded datasets that account for equal identity, appearance and representations are also some of its product offerings. Organizations can deploy this technology across API documentation, teleconferencing, digital humans, identity verification and driver monitoring use cases. With 89% of tech executives believing that synthetic data would transform its industry, Synthesis.ais technology may be a great fit.

Tealiums data orchestration platform is positioned as a universal data hub for businesses seeking a robust customer data platform (CDP) for marketing engagement. This CDP provider offers a tray of solutions in its customer data integration system that allows businesses to connect better with their customers. Tealiums offerings include a tag management system for enterprises to track and unify its digital marketing deployments (Tealium iQ), an API hub to facilitate enterprise interconnectedness, an ML-powered data platform (Tealium AudienceStream) and data management solutions.

The company recently sponsored a comprehensive economic impact study from Forrester, calculating ROI on reference customers.

Coro provides holistic cybersecurity solutions for mid-market and small to medium-sized. The platform leverages AI to identify and remediate malware, ransomware, phishing and bot security threats across all endpoints while reducing the need for a dedicated IT team. In addition, its built on the principle of non-disruptive security, allowing it to provide security solutions for organizations with limited security budgets and expertise.

This cybersecurity-as-a-service (CaaS) vendor shows how AI can support higher-level services brought to lower-level business market tiers.

As AI-powered technologies continue to advance and more organizations adopt them, IT leaders must be sure to ask themselves how the solutions they choose fit into their goals as a business. With so many vendors riding the wave of AI innovation, buyers must select their solutions carefully.

IDC predicts that AI platforms and AI application development and deployment will continue to be the fastest-growing sectors of the AI market. This list provides a starting point for organizations to evaluate the approaches and solutions that best fit their needs.

Read next:New AI software cuts development time dramatically

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Many futurists insist that technological advances will enable humans to upload our minds into computer systems, thereby allowing us to live forever, defying our biological limitations. This concept is deeply flawed but has gained popular attention in recent years. So much so, Amazon has a TV series based on the premise called Upload, not to mention countless other pop-culture references.

As background, the concept of mind uploading is rooted in the very reasonable premise that the human brain, like any system that obeys the laws of physics, can be modeled in software if sufficient computing power is devoted to the problem. To be clear, mind-uploading is not about modeling human brains in the abstract, but modeling specific people, their unique minds represented in such detail that every neuron is accurately simulated, including the massive tangle of connections among them.

Of course, this is an extremely challenging task. There are more than 85 billion neurons in your brain, each with thousands of links to other neurons.Thats around 100 trillion connections a thousand times more than the number of stars in the Milky Way. Its those trillions of connections that make you who you are your personality and memories, your fears and skills and ambitions.To reproduce your mind in software (sometimes called an infomorph), a computer system would need to precisely simulate the vast majority of those connections down to their most subtle interactions.

That level of modeling will not be done by hand. Futurists who believe in mind uploading often envision an automated process using some kind of super-charged MRI machine, that captures the biology down to the molecular level.They further envision the use of artificial intelligence (AI) software to turn that detailed scan into a simulation of each unique neuron and its thousands of connections to other neurons.

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This is a wildly challenging task but is theoretically feasible. It is also theoretically feasible that large numbers of simulated minds could coexist inside a rich simulation of physical reality.Still, the notion that mind uploading will enable any biological human to extend their life is deeply flawed.

The real issue is that the key words in that prior sentence are their life.While it is theoretically possible with sufficient technological advances to copy and reproduce the form and function of a unique human brain within a computer simulation, that human who was copied would still exist in their biological body. Their brain would still be safely housed inside their skull.

The person that would exist in the computer would be a copy.

In other words, if you signed yourself up for mind uploading, you would not feel like you suddenly transported yourself into a computer simulation.In fact, you would not feel anything at all. The brain copying process could have happened without your knowledge while you were asleep or sedated, and you wouldnt have the slightest inkling that a reproduction of your mind existed in a simulation.

We can think of the copy as a digital clone or twin, but it would not be you.It would be a mental copy of you, including all of your memories up to the moment your brain was scanned.But from that time on, the copy would generate its own memories inside whatever simulated world it was installed in. It might interact with other simulated people, learning new things and having new experiences.Or maybe it would interact with the physical world through robotic interfaces.At the same time, the biological you would be generating new memories and skills and knowledge.

In other words, your biological mind and your digital copy would immediately begin to diverge. They would be identical for one instant and then grow apart. Your skills and abilities would diverge.Your knowledge and understanding would diverge. Your personality and objectives would diverge.After a few years, there would be significant differences. And yet, both versions would feel like the real you.

This is a critical point the copy would have the same feelings of individuality that you have. It would feel just as entitled to own its own property and earn its own wages and make its own decisions.In fact, you and the copy would likely have a dispute as to who gets to use your name, as you would both feel like you had used it your entire life.

If I made a copy of myself, it would wake up in a simulated reality and fully believe it was the real Louis Barry Rosenberg, a lifelong technologist. If it were able to interact with the physical world through robotic means, the copy would feel like it had every right to live in my house and drive my car and go to my job.After all, the copy would remember buying that house and getting that job and doing everything else that I can remember doing.

In other words, creating a digital copy through mind uploading has nothing to do with allowing you to live forever. Instead, it would create a competitor who has identical skills, capabilities, and memories and who feels equally justified to be the owner of your identity.

And yes, the copy would feel equally married to your spouse and parent to your children. In fact, if this technology was possible, we could imagine the digital copy suing you for joint custody of your kids, or at least visitation rights.

To address the paradox of creating a copy of an individual rather than enabling digital immortality, some futurists suggest an alternate approach. Instead of scanning and uploading a mind to a computer, they hypothesize the possibility of gradually transforming a persons brain, neuron by neuron, to a non-biological substrate. This is often referred to as cyborging rather than uploading and is an even more challenging technical task than scanning and simulating. In addition, its unclear if gradual replacement actually solves the identity problem, so Id call this direction uncertain at best.

All this said, mind uploading is not the clear path to immortality that is represented in popular culture. Most likely, its a path for creating a duplicate that would react exactly the way you would if you woke up one day and were told Sorry, I know you remember getting married and having kids and a career, but your spouse isnt really your spouse and your kids arent really your kids and your job isnt really...

Is that something anyone would want to subject a copy of yourself to?

Personally, I see this as deeply unethical. So unethical, I wrote a cautionary graphic novel over a decade ago called UPGRADE that explores the dangers of mind uploading. The book takes place in a future world where everyone spends the majority of their lives in the metaverse.

What the inhabitants of this world dont realize is that their lives in the metaverse are continuously profiled by an AI system that observes all their actions and reactions, so it can build a digital model of their minds from a behavioral perspective (no scanning required). When the profiles are complete, the fictional AI convinces people to upgrade themselves by ending their life and allowing their digital copies to fully replace them.

When I wrote that book 14 years ago, it was intended as irony. And yet theres an emerging field today that is headed in this very direction. Euphemistically called the digital afterlife industry, there are many startups pushing to digitize loved ones so that family members can interact with them after their death. There are even startups that want to profile your actions in the metaverse so you too can live forever in their digital world. Even Amazon recently stepped into this space by demonstrating how Alexa can clone the voice of your dead grandmother and allow you to hear her speak.

With so much activity in this space, how long before a startup begins touting the cost-saving benefits of ending your life early and allowing your digital replacement to live on? I fear its just a matter of time.My only hope is that entrepreneurs will be honest with the public about the reality of mind uploading its not a pathway to immortality.

At least, not the way many people think.

Louis Rosenberg, Ph.D., is a pioneer in fields of VR, AR and AI. He earned his Ph.D. from Stanford University, has been awarded over 300 patents, and founded a number of successful companies. Rosenberg began his work at Air Force Research Laboratorywhere he developed the first functional augmented reality system to merge real and virtual worlds. Rosenberg is currently CEO of Unanimous AI, the chief scientist of the Responsible Metaverse Alliance and global technology advisor to the XR Safety Initiative (XRSI).

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