Collaborating Authors


Recent and forthcoming machine learning and AI seminars: January 2021 edition


This post contains a list of the AI-related seminars that are scheduled to take place between now and the end of February 2021. We've also listed recent past seminars that are available for you to watch. All events detailed here are free and open for anyone to attend virtually. This list includes forthcoming seminars scheduled to take place between 15 January and 28 February. Zero-shot (human-AI) coordination (in Hanabi) and ridge rider Speaker: Jakob Foerster (Facebook, University of Toronto & Vector Institute) Organised by: University College London Zoom link is here.

AI-Powered Text From This Program Could Fool the Government


In October 2019, Idaho proposed changing its Medicaid program. The state needed approval from the federal government, which solicited public feedback via But half came not from concerned citizens or even internet trolls. They were generated by artificial intelligence. And a study found that people could not distinguish the real comments from the fake ones.

The language of a virus


Uncovering connections between seemingly unrelated branches of science might accelerate research in one branch by using the methods developed in the other branch as stepping stones. On page 284 of this issue, Hie et al. ([ 1 ][1]) provide an elegant example of such unexpected connections. The authors have uncovered a parallel between the properties of a virus and its interpretation by the host immune system and the properties of a sentence in natural language and its interpretation by a human. By leveraging an extensive natural language processing (NLP) toolbox ([ 2 ][2], [ 3 ][3]) developed over the years, they have come up with a powerful new method for the identification of mutations that allow a virus to escape from recognition by neutralizing antibodies. In 1950, Alan Turing predicted that machines will eventually compete with men in “intellectual fields” and suggested that one possible way forward would be to build a machine that can be taught to understand and speak English ([ 4 ][4]). This was, and still is, an ambitious goal. It is clear that language grammar can provide a formal skeleton for building sentences, but how can machines be trained to infer the meanings? In natural language, there are many ways to express the same idea, and yet small changes in expression can often change the meaning. Linguistics developed a way of quantifying the similarity of meaning (semantics). Specifically, it was proposed that words that are used in the same context are likely to have similar meanings ([ 5 ][5], [ 6 ][6]). This distributional hypothesis became a key feature for the computational technique in NLP, known as word (semantic) embedding. The main idea is to characterize words as vectors that represent distributional properties in a large amount of language data and then embed these sparse, high-dimensional vectors into more manageable, low-dimensional space in a distance-preserving manner. By the distributional hypothesis, this technique should group words that have similar semantics together in the embedding space. Hie et al. proposed that viruses can also be thought to have a grammar and semantics. Intuitively, the grammar describes which sequences make specific viruses (or their parts). Biologically, a viral protein sequence should have all the properties needed to invade a host, multiply, and continue invading another host. Thus, in some way, the grammar represents the fitness of a virus. With enough data, current machine learning approaches can be used to learn this sequence-based fitness function. ![Figure][7] Predicting immune escape The constrained semantic change search algorithm obtains semantic embeddings of all mutated protein sequences using bidirectional long short-term memory (LSTM). The sequences are ranked according to the combined score of the semantic change (the distance of a mutation from the original sequence) and fitness (the probability that a mutation appears in viral sequences). GRAPHIC: V. ALTOUNIAN/SCIENCE But what would be the meaning (semantics) of a virus? Hie et al. suggested that the semantics of a virus should be defined in terms of its recognition by immune systems. Specifically, viruses with different semantics would require a different state of the immune system (for example, different antibodies) to be recognized. The authors hypothesized that semantic embeddings allow sequences that require different immune responses to be uncovered. In this context, words represent protein sequences (or protein fragments), and recognition of such protein fragments is the task performed by the immune system. To escape immune responses, viral genomes can become mutated so that the virus evolves to no longer be recognized by the immune system. However, a virus that acquires a mutation that compromises its function (and thus fitness) will not survive. Using the NLP analogy, immune escape will be achieved by the mutations that change the semantics of the virus while maintaining its grammaticality so that the virus will remain infectious but escape the immune system. On the basis of this idea, Hie et al. developed a new approach, called constrained semantic change search (CSCS). Computationally, the goal of CSCS is to identify mutations that confer high fitness and substantial semantic changes at the same time (see the figure). The immune escape scores are computed by combining the two quantities. The search algorithm builds on a powerful deep learning technique for language modeling, called long short-term memory (LSTM), to obtain semantic embeddings of all mutated sequences and rank the sequences according to their immune escape scores in the embedded space. The semantic changes correspond to the distance of the mutated sequences to the original sequence in the semantic embedding, and its “grammaticality” (or fitness) is estimated by the probability that the mutation appears in viral sequences. The immune escape scores can then be computed by simultaneously considering both the semantic distance and fitness probability. Hie et al. confirmed their hypothesis for the correspondence of grammaticality and semantics to fitness and immune response in three viral proteins: influenza A hemagglutinin (HA), HIV-1 envelope (Env), and severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) Spike. For the analogy of semantics to immune response, they found that clusters of semantically similar viruses were in good correspondence with virus subtypes, host, or both, confirming that the language model can extract functional meanings from protein sequences. The clustering patterns also revealed interspecies transmissibility and antigenic similarity. The correspondence of grammaticality to fitness was assessed more directly by using deep mutational scans evaluated for replication fitness (for HA and Env) or binding (for Spike). The combined model was tested against experimentally verified mutations that allow for immue escape. Scoring each amino acid residue with CSCS, the authors uncovered viral protein regions that are significantly enriched with escape potential: the head of HA for influenza, the V1/V2 hypervariable regions for HIV Env, and the receptor-binding domain (RBD) and amino-terminal domain for SARS-CoV-2 Spike. The language of viral evolution and escape proposed by Hie et al. provides a powerful framework for predicting mutations that lead to viral escape. However, interesting questions remain. Further extending the natural language analogy, it is notable that individuals can interpret the same English sentence differently depending on their past experience and the fluency in the language. Similarly, immune response differs between individuals depending on factors such as past pathogenic exposures and overall “strength” of the immune system. It will be interesting to see whether the proposed approach can be adapted to provide a “personalized” view of the language of virus evolution. 1. [↵][8]1. B. Hie, 2. E. Zhong, 3. B. Berger, 4. B. Bryson , Science 371, 284 (2021). [OpenUrl][9][Abstract/FREE Full Text][10] 2. [↵][11]1. L. Yann, 2. Y. Bengio, 3. G. Hinton , Nature 521, 436 (2015). [OpenUrl][12][CrossRef][13][PubMed][14] 3. [↵][15]1. T. Young, 2. D. Hazarika, 3. S. Poria, 4. E. Cambria , IEEE Comput. Intell. Mag. 13, 55 (2018). [OpenUrl][16] 4. [↵][17]1. A. Turing , Mind LIX, 433 (1950). 5. [↵][18]1. Z. S. Harris , Word 10, 146 (1954). [OpenUrl][19][CrossRef][20][PubMed][21] 6. [↵][22]1. J. R. Firth , in Studies in Linguistic Analysis (1957), pp. 1–32. Acknowledgments: The authors are supported by the Intramural Research Programs of the National Library of Medicine at the National Institutes of Health, USA. [1]: #ref-1 [2]: #ref-2 [3]: #ref-3 [4]: #ref-4 [5]: #ref-5 [6]: #ref-6 [7]: pending:yes [8]: #xref-ref-1-1 "View reference 1 in text" [9]: {openurl}?query=rft.jtitle%253DScience%26rft.stitle%253DScience%26rft.aulast%253DHie%26rft.auinit1%253DB.%26rft.volume%253D371%26rft.issue%253D6526%26rft.spage%253D284%26rft.epage%253D288%26rft.atitle%253DLearning%2Bthe%2Blanguage%2Bof%2Bviral%2Bevolution%2Band%2Bescape%26rft_id%253Dinfo%253Adoi%252F10.1126%252Fscience.abd7331%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx [10]: /lookup/ijlink/YTozOntzOjQ6InBhdGgiO3M6MTQ6Ii9sb29rdXAvaWpsaW5rIjtzOjU6InF1ZXJ5IjthOjQ6e3M6ODoibGlua1R5cGUiO3M6NDoiQUJTVCI7czoxMToiam91cm5hbENvZGUiO3M6Mzoic2NpIjtzOjU6InJlc2lkIjtzOjEyOiIzNzEvNjUyNi8yODQiO3M6NDoiYXRvbSI7czoyMjoiL3NjaS8zNzEvNjUyNi8yMzMuYXRvbSI7fXM6ODoiZnJhZ21lbnQiO3M6MDoiIjt9 [11]: #xref-ref-2-1 "View reference 2 in text" [12]: {openurl}?query=rft.jtitle%253DNature%26rft.volume%253D521%26rft.spage%253D436%26rft_id%253Dinfo%253Adoi%252F10.1038%252Fnature14539%26rft_id%253Dinfo%253Apmid%252F26017442%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx [13]: /lookup/external-ref?access_num=10.1038/nature14539&link_type=DOI [14]: /lookup/external-ref?access_num=26017442&link_type=MED&atom=%2Fsci%2F371%2F6526%2F233.atom [15]: #xref-ref-3-1 "View reference 3 in text" [16]: {openurl}?query=rft.jtitle%253DIEEE%2BComput.%2BIntell.%2BMag.%26rft.volume%253D13%26rft.spage%253D55%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx [17]: #xref-ref-4-1 "View reference 4 in text" [18]: #xref-ref-5-1 "View reference 5 in text" [19]: {openurl}?query=rft.jtitle%253DWord%26rft.volume%253D10%26rft.spage%253D146%26rft_id%253Dinfo%253Adoi%252F10.1080%252F00437956.1954.11659520%26rft_id%253Dinfo%253Apmid%252F32513867%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx [20]: /lookup/external-ref?access_num=10.1080/00437956.1954.11659520&link_type=DOI [21]: /lookup/external-ref?access_num=32513867&link_type=MED&atom=%2Fsci%2F371%2F6526%2F233.atom [22]: #xref-ref-6-1 "View reference 6 in text"

L.A. County will stop using Curative coronavirus test after concerns from the FDA

Los Angeles Times

Los Angeles County health officials said Sunday they will stop providing a commonly used coronavirus test after federal regulators raised questions about its accuracy. The decision affects only a small number of county-supported mobile testing sites. County health officials had already discontinued the broad use of oral swab tests produced by Silicon Valley start-up Curative over the summer because of concerns about too many false negatives. The use of Curative oral swab tests at the city of Los Angeles' 10 drive-through testing sites, including the massive facility at Dodger Stadium, are unaffected by Sunday's decision. Mayor Eric Garcetti has defended the tests as broadly effective and said that moving away from them could lead to fewer people being diagnosed and greater spread of the virus.

Using artificial intelligence to find new uses for existing medications


The intent of this work is to speed up drug repurposing, which is not a new concept -- think Botox injections, first approved to treat crossed eyes and now a migraine treatment and top cosmetic strategy to reduce the appearance of wrinkles. But getting to those new uses typically involves a mix of serendipity and time-consuming and expensive randomized clinical trials to ensure that a drug deemed effective for one disorder will be useful as a treatment for something else. The Ohio State University researchers created a framework that combines enormous patient care-related datasets with high-powered computation to arrive at repurposed drug candidates and the estimated effects of those existing medications on a defined set of outcomes. Though this study focused on proposed repurposing of drugs to prevent heart failure and stroke in patients with coronary artery disease, the framework is flexible -- and could be applied to most diseases. "This work shows how artificial intelligence can be used to'test' a drug on a patient, and speed up hypothesis generation and potentially speed up a clinical trial," said senior author Ping Zhang, assistant professor of computer science and engineering and biomedical informatics at Ohio State.

FDA warns UK coronavirus variant may result in false-negative tests

FOX News

Former CDC Director Dr. Tom Frieden says the new strain increases the urgency for vaccines and wearing masks. The Food and Drug Administration (FDA) on Friday issued an alert about the impact viral mutations of the coronavirus may have, including the potential to result in false negative tests. The variant, B.1.1.7 was first discovered in the U.K. several weeks ago, and has been confirmed in over 50 cases in the U.S. so far. "The Food and Drug Administration is alerting clinical laboratory staff and health care providers that the FDA is monitoring the potential impact of viral mutations, including an emerging variant from the United Kingdom known as the B.1.1.7 variant, on authorized SARS-CoV-2 molecular tests, and that false negative results can occur with any molecular test for the detection of SARS-CoV-2 if a mutation occurs on the part of the virus's genome assessed by that test," the FDA said. "The SARS-CoV-2 virus can mutate over time, like all viruses, resulting in genetic variation in the population of circulating viral strains, as seen with the B.1.1.7 variant."

L.A. using coronavirus test that FDA warns may produce false negatives

Los Angeles Times

The coronavirus test being provided daily to tens of thousands of residents in Los Angeles and other parts of California may be producing inaccurate results, according to a warning from federal officials that could raise questions about the accuracy of infection data shaping the pandemic response. The guidance from the Food and Drug Administration warns healthcare providers and patients that the test made by Curative, a year-old Silicon Valley start-up that supplies the oral-swab tests at L.A.'s 10 drive-through testing sites, carries a "risk of false results, particularly false negative results." To reduce the risk of false negatives, the Curative test should be used only on "symptomatic individuals within 14 days of COVID-19 symptom onset," and the swab should be observed and directed by a healthcare worker, the FDA said. The guidance, issued Monday, repeats the instructions that the FDA issued when the test was first granted an emergency-use authorization. The FDA warning appears to sharply contradict Los Angeles Mayor Eric Garcetti, who in April made coronavirus testing available to anyone, regardless of symptoms.

COVID-19 testing: One size does not fit all


Tests for detecting severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) were developed within days of the release of the virus genome ([ 1 ][1]). Multiple countries have been successful at controlling SARS-CoV-2 transmission by investing in large-scale testing capacity ([ 2 ][2]). Most testing has focused on quantitative polymerase chain reaction (qPCR) assays, which are capable of detecting minute amounts of viral RNA. Although powerful, these molecular tools cannot be scaled to meet demands for more extensive public health testing. To combat COVID-19, the “one-size-fits-all” approach that has dominated and confused decision-making with regard to testing and the evaluation of tests is unsuitable: Diagnostics, screening, and surveillance serve different purposes, demand distinct strategies, and require separate approval mechanisms. By supporting the innovation, approval, manufacturing, and distribution of simpler and cheaper screening and surveillance tools, it will be possible to more effectively limit the spread of COVID-19 and respond to future pandemics. Many types of tests are available for COVID-19 for clinical and public health use (see the figure). Testing can be performed in a central laboratory, at the point of care (POC), or in the community at the workplace, school, or home. COVID-19 testing begins with specimen collection. For medical use, a nasopharyngeal swab collected by a health care professional has been used for detection of virus infections. Demands on testing throughput for COVID-19, however, have driven new collection approaches, including saliva and less invasive nasal swabs. COVID-19 tests include molecular tests such as qPCR, isothermal amplification, and CRISPR, as well as antigen tests that detect SARS-CoV-2 proteins directly. Although rapid antigen tests have lower analytical sensitivity (i.e., require greater amounts of virus material to turn positive) than qPCR-based tests, their ability to detect infectious individuals with culturable virus is as high as for qPCR ([ 3 ][3]). Specificity (i.e., correctly identifying those not infected with SARS-CoV-2) of antigen tests achieves comparable results to molecular tests ([ 4 ][4]). Diagnostic testing for COVID-19 focuses on accurately identifying patients who are infected with SARS-CoV-2 to establish the presence or absence of disease and is performed on symptomatic patients or asymptomatic individuals who are at high risk of infection. This type of testing requires assays that are highly sensitive, so as to not miss COVID-19 patients (false negatives), and specific, so as to not wrongly diagnose SARS-CoV-2–negative individuals as having COVID-19 (false positives). These tests are typically performed by centralized high-complexity laboratories with specialized equipment using qPCR assays, with results that can be reported within 12 to 48 hours. Major bottlenecks in testing, however, have led to turnaround times exceeding 5 to 10 days in some regions, making such tests useless to prevent transmission. POC diagnostic testing at medical facilities can be qPCR assays, isothermal amplification, or antigen-based ([ 4 ][4]). These POC tests often require instruments that run a limited number of tests and can return results in under an hour. The need for an instrument limits the number of tests that can be performed and where they can be used. However, newer antigen tests are becoming available that do not require instruments or skilled operators, potentially allowing for much more distributed POC testing. Surveillance testing of populations can be used both as a tool for understanding historical exposures and as a measure of ongoing community transmission. For the former, serological testing of individuals for the presence of SARS-CoV-2–specific antibodies is used to identify those previously infected. For the latter, surveillance testing can be an effective way to monitor real-time SARS-CoV-2 spread in communities. One promising method is wastewater surveillance, which has been used to assess community transmission of poliovirus ([ 5 ][5]) and has shown potential for COVID-19 ([ 6 ][6]). qPCR testing of wastewater is used to detect SARS-CoV-2, and frequency dynamics of viral genetic material indicate COVID-19 infections in a community. Surveillance can also be performed from swab or saliva samples taken directly from individuals, and, in populations with low COVID-19 prevalence, pooling can be used to increase capacity and lower cost. For surveillance testing, the goal is not identification of every case but rather the collection of data from representative samples that accurately measure prevalence and serve to inform public health policy and resource allocation. Because the focus is on extrapolations to the population and not the individual, tests with known deviations from 100% sensitivity and specificity are still appropriate when the variance can be statistically corrected ([ 7 ][7]). To be most effective, results should include reported qPCR cycle thresholds, which is an estimate of viral load ([ 7 ][7]), to model epidemic trajectory and allow for real-time evaluation of mitigation programs ([ 8 ][8]), including once vaccination programs have begun. Screening testing of asymptomatic individuals to detect people who are likely infectious has been critically underused yet is one of the most promising tools to combat the COVID-19 pandemic ([ 9 ][9]). Infection with SARS-CoV-2 does not lead to symptoms in ∼20 to 40% of cases, and symptomatic disease is preceded by a presymptomatic incubation period ([ 10 ][10]). However, asymptomatic and presymptomatic cases are key contributors to virus spread, complicating our ability to break transmission chains ([ 10 ][10]). Entry screening to detect infectious individuals before accessing facilities (e.g., nursing homes, restaurants, and airports), along with symptom screening and temperature checks, can be beneficial, particularly in high-risk facilities such as skilled nursing facilities. When used strategically, entry-screening measures can be effective at suppressing transmission. Entry screening requires testing that provides rapid results—ideally within 15 min—to be most effective. The required sensitivity and specificity of entry-screening tests are, like all tests, context dependent. Entry-screening tests for a nursing home, for example, must be highly sensitive because the consequences of bringing SARS-CoV-2 into a nursing home can be devastating. Such tests must also be highly specific because the consequences of grouping a false-positive person with COVID-19–positive individuals could be deadly. Conversely, because children have substantially reduced mortality from COVID-19, entry screening into schools might require greater compromise that balances resources and sensitivity to test as many individuals as possible with a need to minimize disruptive false positives. Key to use of tests for entrance screening is that a negative test alone should not be considered sufficient to enter—that should be based on satisfying other requirements, including masks and physical distancing. Conversely, a positive test should be sufficient to bar entry in most settings. Public health screening is potentially the most powerful form of COVID-19 testing, aimed at outbreak suppression through maximizing detection of infectious individuals. This type of screening entails frequent serial testing of large fractions of the population, through self-administered at-home rapid tests, or in the community at high-contact settings, such as schools and workplaces ([ 9 ][9]). Public health screening can achieve herd effects by stopping onward spread through detection of asymptomatic or presymptomatic cases (fig. S1). Notably, not every transmission chain needs to be severed to achieve herd effects. Mathematical models that incorporate relevant variation in viral loads and test accuracy suggest that with frequent testing of a large fraction of a population, a sufficient number of cases could be detected to create herd effects ([ 11 ][11]). For example, Slovakia undertook public health screening to address COVID-19 ([ 12 ][12]): During a 2-week period, ∼80% of the population was screened using rapid antigen tests. With 50,000 cases identified, combined with other public health measures, it reduced incidence by 82% within 2 weeks ([ 12 ][12]). An important feature of large-scale public health screening is that centrally controlled reporting and contact tracing programs are not essential to induce herd effects as they are for surveillance testing. In a robust public health screening program, sufficient numbers of people are routinely testing themselves, such that contact tracing is subsumed by the screening program ([ 11 ][11]). Similar to home pregnancy tests, screening tests should be easy to obtain and administer, fast, and cheap. Like diagnostic tests, these tests must produce very low false-positive rates. If a screening test does not achieve high-enough specificity (e.g., >99.9%), screening programs can be paired with secondary confirmatory testing. Unlike diagnostic tests, however, the sensitivity of screening tests should not be determined based on their ability to diagnose patients but rather by their ability to accurately identify people who are most at risk of transmitting SARS-CoV-2. Such individuals tend to have higher viral loads ([ 13 ][13]), which makes the virus easier to detect ([ 14 ][14]). A focus on identifying infectious people means that frequency and abundance of tests should be prioritized above achieving high analytical sensitivity ([ 11 ][11]). Indeed, loss in sensitivity of individual tests, within reason, can be compensated for by frequency of testing and wider dissemination of tests ([ 9 ][9]). In addition, public health messaging should ensure appropriate expectations of screening, particularly around sensitivity and specificity so that false negatives and false positives do not erode public trust. ![Figure][15] COVID-19 testing strategies Testing for SARS-CoV-2 can be for personal or population health. Collection can be from symptomatic or asymptomatic individuals, as well as from wastewater and swabs of surfaces. The tests may be performed in central laboratories, at the POC, or using rapid tests. Attributes of tests differ according to application. GRAPHIC: KELLIE HOLOSKI/ SCIENCE Tests for public health screening require rapid, decentralized solutions that can be scaled for frequent screening of large numbers of asymptomatic individuals. Lateral-flow antigen tests and upcoming paper-based synthetic biology and CRISPR-based assays fit these needs and could be scaled to tens of millions of daily tests ([ 9 ][9]). These tests are simple and cheap, can be self-administered, and do not require machines to run and return results. The Abbott BinaxNOW rapid antigen test, which recently received an Emergency Use Authorization (EUA) in the United States as a diagnostic device, also comes with a smartphone app, allowing self-reporting of COVID-19 status that could be used instead of centralized reporting by public health agencies. Critically, despite being shown to be highly effective at detecting infectious individuals ([ 14 ][14]), very few of these tests are currently approved for screening of asymptomatic individuals, substantially limiting their utility. If such tests were made available direct to consumer (priced to allow equitable access) or produced and provided free of charge by governments, individuals could obtain their COVID-19 status at their own choosing and without complex medical decisions. Testing is a central pillar of clinical and public health response to global health emergencies, including the COVID-19 pandemic. Nearly all testing modalities have a role, and the one-size-fits-all approach to testing by many Western countries has failed. Many lower- and middle-income countries—including Senegal, Vietnam, and Ghana—have fared far better in their COVID-19 response, often using strong testing programs. The focus on diagnostic tests and the use of preexisting authorization pathways focused on qPCR-based clinical diagnostics not only slows the development and deployment of new surveillance and screening tests but also confuses the picture of what metrics effective public health tools should achieve. Testing to diagnose a patient with COVID-19 is fundamentally different from testing a person to prevent onward transmission. Regulatory pathways should be modified to incorporate these differences so that public health and screening tests are appropriately evaluated. 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An Existential Crisis in Neuroscience - Issue 94: Evolving


This week we are reprinting our top stories of 2020. This article first appeared online in our "Maps" issue in January, 2020. On a chilly evening last fall, I stared into nothingness out of the floor-to-ceiling windows in my office on the outskirts of Harvard's campus. As a purplish-red sun set, I sat brooding over my dataset on rat brains. I thought of the cold windowless rooms in downtown Boston, home to Harvard's high-performance computing center, where computer servers were holding on to a precious 48 terabytes of my data. I have recorded the 13 trillion numbers in this dataset as part of my Ph.D. experiments, asking how the visual parts of the rat brain respond to movement. Printed on paper, the dataset would fill 116 billion pages, double-spaced. When I recently finished writing the story of my data, the magnum opus fit on fewer than two dozen printed pages. Performing the experiments turned out to be the easy part.

Top 100 Artificial Intelligence Companies in the World


Artificial Intelligence (AI) is not just a buzzword, but a crucial part of the technology landscape. AI is changing every industry and business function, which results in increased interest in its applications, subdomains and related fields. This makes AI companies the top leaders driving the technology swift. AI helps us to optimise and automate crucial business processes, gather essential data and transform the world, one step at a time. From Google and Amazon to Apple and Microsoft, every major tech company is dedicating resources to breakthroughs in artificial intelligence. As big enterprises are busy acquiring or merging with other emerging inventions, small AI companies are also working hard to develop their own intelligent technology and services. By leveraging artificial intelligence, organizations get an innovative edge in the digital age. AI consults are also working to provide companies with expertise that can help them grow. In this digital era, AI is also a significant place for investment. AI companies are constantly developing the latest products to provide the simplest solutions. Henceforth, Analytics Insight brings you the list of top 100 AI companies that are leading the technology drive towards a better tomorrow. AEye develops advanced vision hardware, software, and algorithms that act as the eyes and visual cortex of autonomous vehicles. AEye is an artificial perception pioneer and creator of iDAR, a new form of intelligent data collection that acts as the eyes and visual cortex of autonomous vehicles. Since its demonstration of its solid state LiDAR scanner in 2013, AEye has pioneered breakthroughs in intelligent sensing. Their mission was to acquire the most information with the fewest ones and zeros. This would allow AEye to drive the automotive industry into the next realm of autonomy. Algorithmia invented the AI Layer.