I have to admit it: I backed the wrong horse when it came to driving home automation from a digital assistant and went with Microsoft's Cortana and its Harmon Kardon Invoke smart speakers. I had good enough reason: I trusted Microsoft's privacy commitments a lot more than either Amazon's or Google's, and Apple's Home relied on the too-expensive HomePod smart speakers. Sure, I had a couple of Amazon Echoes and a Google Nest Mini to try out those ecosystems, but their over-reliance on in-cloud voice recognition was that bit too much on the creepy side. Still, I could happily control my Hue lights from Cortana, though support for my Netatmo thermostats and Ikea Tradfri lights had to be through maker tools like IFTTT and workflow automation with webhook APIs like Power Automate or Zapier. But then Microsoft refocused Cortana on its commercial customers and announced that its Invoke Cortana integrations were due to be turned off early in 2021.

Parity is among a growing crop of startups promising organizations ways to develop, monitor, and fix their AI models. They offer a range of products and services from bias-mitigation tools to explainability platforms. Initially most of their clients came from heavily regulated industries like finance and health care. But increased research and media attention on issues of bias, privacy, and transparency have shifted the focus of the conversation. New clients are often simply worried about being responsible, while others want to "future proof" themselves in anticipation of regulation.

Artificial Intelligence (AI) has come a long way over the past few years in simulating human intelligence. Today, AI is the lifeblood of almost every organisation cutting across sectors including, retail, financial, healthcare, among others. Here's an updated list of 10 best intro books on artificial intelligence geared towards AI enthusiasts. About: Mathematics and statistics are the backbone of artificial intelligence. This book is perfect for understanding the basics and the mathematics behind AI.

The field of artificial intelligence (AI) has created computers that can drive cars, synthesize chemical compounds, fold proteins and detect high-energy particles at a superhuman level. However, these AI algorithms cannot explain the thought processes behind their decisions. A computer that masters protein folding and also tells researchers more about the rules of biology is much more useful than a computer that folds proteins without explanation. Therefore, AI researchers like me are now turning our efforts toward developing AI algorithms that can explain themselves in a manner that humans can understand. If we can do this, I believe that AI will be able to uncover and teach people new facts about the world that have not yet been discovered, leading to new innovations.

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"

Viral mutations that evade neutralizing antibodies, an occurrence known as viral escape, can occur and may impede the development of vaccines. To predict which mutations may lead to viral escape, Hie et al. used a machine learning technique for natural language processing with two components: grammar (or syntax) and meaning (or semantics) (see the Perspective by Kim and Przytycka). Three different unsupervised language models were constructed for influenza A hemagglutinin, HIV-1 envelope glycoprotein, and severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) spike glycoprotein. Semantic landscapes for these viruses predicted viral escape mutations that produce sequences that are syntactically and/or grammatically correct but effectively different in semantics and thus able to evade the immune system. Science , this issue p. [284][1]; see also p. [233][2] The ability for viruses to mutate and evade the human immune system and cause infection, called viral escape, remains an obstacle to antiviral and vaccine development. Understanding the complex rules that govern escape could inform therapeutic design. We modeled viral escape with machine learning algorithms originally developed for human natural language. We identified escape mutations as those that preserve viral infectivity but cause a virus to look different to the immune system, akin to word changes that preserve a sentence’s grammaticality but change its meaning. With this approach, language models of influenza hemagglutinin, HIV-1 envelope glycoprotein (HIV Env), and severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) Spike viral proteins can accurately predict structural escape patterns using sequence data alone. Our study represents a promising conceptual bridge between natural language and viral evolution. [1]: /lookup/doi/10.1126/science.abd7331 [2]: /lookup/doi/10.1126/science.abf6894

The year 2020 was profoundly challenging for citizens, companies, and governments around the world. As covid-19 spread, requiring far-reaching health and safety restrictions, artificial intelligence (AI) applications played a crucial role in saving lives and fostering economic resilience. Research and development (R&D) to enhance core AI capabilities, from autonomous driving and natural language processing to quantum computing, continued unabated. Baidu was at the forefront of many important AI breakthroughs in 2020. This article outlines five significant advances with implications for combating covid-19 as well as transforming the future of our economies and society.

Google is offering a free course for people who are on the hunt for skills to use containers, big data and machine-learning models in Google Cloud. The initial batch of courses consists of four tracks aimed at data analysts, cloud architects, data scientists and machine-learning engineers. The January 2021 course offers a fast track to understand key tools for engineers and architects to use in Google Cloud. It includes a series on getting started in Google Cloud, another focussing on its BigQuery data warehouse, one that delves into the Kubernetes engine for managing containers, another for the Anthos application management platform, and a final chapter on Google's standard interfaces for natural language processing and computer vision AI. Participants need to sign up to Google's "skills challenge" and will be given 30 days' free access to Google Cloud labs.

Google has introduced Guest Mode to Google Assistant to give users the chance to ensure their interactions with their Google smart speakers or displays, including Nest Audio and Nest Hub Max, are not saved to their account when this new mode is switched on. When Guest Mode is switched on, users will be able to continue to ask questions, control smart home devices, set timers, and play music, but will not be able to access personal results, such as calendar entries or contacts, until Guest Mode is switched off. Google added the device will also automatically delete audio recordings and Google Assistant activity from the device owner's account when in Guest Mode. However, if users are interacting with other apps and services, such as Google Maps, YouTube, or other media and smart home services while in Guest Mode, those apps may still save that activity, Google said. To switch on Guest Mode, it is a matter of users saying, "Hey Google, turn on Guest Mode", before the device plays a special chime and a guest icon is displayed.

Amazon's Echo Studio high-fidelity smart speaker is on sale for only $169.99 as of Jan. 12 -- that's a 15% savings. Up until a few years ago, audio quality always took a backseat to the "smart" part of smart speakers. You didn't buy such a device because you wanted to feel like you were lounging amongst the quaint woodsiness of Long Pond Studio while listening to folklore; you bought it because you wanted a friendly AI to check the weather and order more toilet paper for you. For Amazon's popular Echo lineup, that all changed with 2019's introduction of the Echo Studio, an Alexa-enabled smart speaker geared toward audiophiles. Not only does it support Dolby Atmos for clear, immersive audio, but much like other high-end speakers made by brands like Sonos and Apple, it comes with a "3D sound" feature that automatically adjusts its output based on the acoustics of a room.