Google is infamous for spinning up products and killing them off, often in very short order. But the tech giant's ambitions stretch into many domains that touch human lives these days. And -- it turns out -- so does Google's tendency to kill off products that its PR has previously touted as "life saving". To wit: Following a recent reconfiguration of Google's health efforts -- reported earlier by Business Insider -- the tech giant confirmed to TechCrunch that it is decommissioning its clinician support app, Streams. The app, which Google Health PR bills as a "mobile medical device", was developed back in 2015 by DeepMind, an AI division of Google -- and has been used by the U.K.'s National Health Service in the years since, with a number of NHS Trusts inking deals with DeepMind Health to roll out Streams to their clinicians.

RNA is distinct among large biomolecules in that it has both informational coding ability, carried in its sequence, and the ability to form complex three-dimensional structures that can have catalytic and regulatory roles. The information-carrying component is widely appreciated. The pattern of base pairing—the first level of RNA structure—can be experimentally assessed and modeled with impressive accuracy ([ 1 ][1], [ 2 ][2]). By contrast, our understanding of the extent and roles of complex three-dimensional RNA structures remains rudimentary. RNA viral genomes are rich in motifs with complex three-dimensional structures with regulatory functions ([ 3 ][3]), and evidence increasingly supports the hypothesis that functional RNA structures are ubiquitous in organisms ranging from bacteria to humans. However, developing and testing hypotheses about the roles of RNA structure have been hindered by the inability to identify and model these structures. On page 1047 of this issue, Townshend et al. ([ 4 ][4]) report a machine-learning strategy for identifying native-like RNA folds. Nearly all RNAs that form well-understood complex structures fall into a small number of classes: the ribosomal RNAs, the large and small ribozymes that catalyze RNA cleavage, bacterial riboswitches, and regulatory elements encoded by RNA viruses. Thus, there are limited examples for guiding identification and modeling of RNAs with complex three-dimensional structures. There are only four major RNA nucleotides, and the interactions that govern base pairing and simple helix formation are well understood. Once formed, RNA helices (secondary structure) often assemble as fairly rigid elements that interact hierarchically to form more complicated structures (tertiary structure) (see the figure). Despite these simplifying features, the modeling of complex RNA structures has proven to be difficult. The RNA-Puzzles community exercise ([ 5 ][5], [ 6 ][6]) has been instrumental in illuminating the challenges involved: Groups try to predict an RNA structure from its sequence before learning the solved structure. Several rounds of RNA-Puzzles have revealed important themes. No single method consistently yields the best models, although certain approaches have better records than others, and most approaches are getting better. The best agreement tends to result when experimental or homology-based information is incorporated into the computational modeling. However, the median accuracy for small RNAs, with complex tertiary folds but without a close known homolog, has stayed stubbornly stuck in a range of ∼15- to 20-Å root mean square deviation [(RMSD) a measure of the similarity between known and modeled structures]. This agreement is much poorer than that now achieved for protein structures by machine learning ([ 7 ][7]), where native-like folds (∼2-Å RMSD or less) are achieved. Modeled RNA structures thus often recapitulate the overall fold of a target RNA but do not consistently reveal details of the tertiary structure. Current methods are not likely to be useful for applications such as understanding the biological mechanism of a structure or for designing ligands (or drugs) that modulate RNA function. ![Figure][8] RNA structure RNA molecules have multiple levels of structure and ability to encode information. The sequence of RNA is readily determined. RNA secondary structure can now be elucidated with high levels of accuracy using approaches that meld computational energy minimization with experimental per-nucleotide chemical probing information. Townshend et al. developed a deep neural network that can identify models that best represent the native tertiary state, taking a step toward modeling three-dimensional RNA structure. GRAPHIC: C. BICKEL/ SCIENCE The Atomic Rotationally Equivalent Scorer (ARES) approach of Townshend et al. is a deep neural network, a form of machine learning, and did not initially include preconceived notions of RNA structure. Indeed, the ARES framework is not specific to RNA and can be applied to other problems in molecular structure. Instead, ARES was given a small set of motifs with known RNA structure plus a large number of alternative (incorrect) variations of these same structures. ARES parameters were adjusted so that the program learned the functional and geometric arrangements of each atom and how these elements are positioned relative to each other. Layers in the neural network compute features from finer to coarser scales to recognize base pairs, helices, and more-complex structures. For example, ARES learned patterns of base pairing, the optimal geometry for RNA helices, and a subset of noncanonical tertiary motifs without being provided explicit information about these features of RNA structure. Although ARES was trained on very simple RNA systems, the resulting ARES scoring function was able to predict structures of more complex RNAs, on average, to roughly a 12-Å RMSD. This degree of accuracy represents an overall improvement of ∼4 Å over prior scoring methods. ARES is still short of the level consistent with atomic resolution or sufficient to guide identification of key functional sites or drug discovery efforts, but Townshend et al. have achieved notable progress in a field that has proven recalcitrant to transformative advances. There are three fundamental challenges for modeling complex RNA three-dimensional structures: generating reasonable structures that may represent a biological state, accurately scoring or identifying models that best represent the correct native state, and using these hopefully accurate models to discover new functional motifs and to develop hypotheses regarding the mechanisms by which RNAs with complex three-dimensional structures regulate biological processes. The ARES machine-learning approach addressed the second of these three challenges: Candidate structures still need to be generated for evaluation by ARES. With further development, deep learning strategies hold promise for creating new scoring functions that can guide structure generation in ways that might yield near-native structures. Another important goal is to use a machine-learning strategy to identify regions in large RNAs most likely to fold into three-dimensional structures. Current computational-only algorithms are not able to predict the pattern of base pairing in large RNAs accurately, even though base pairs are simpler to predict than tertiary structure. However, secondary structures for large RNAs are routinely modeled to high accuracies by incorporating experimental information. New, efficiently executed experiments are now being developed that measure features of RNA tertiary structures. Another frontier, analogous to recent advances in secondary structure modeling, would thus be to incorporate experimental information into machine-learning strategies for modeling RNA tertiary structure. Large-scale investigation of RNA structure to date, primarily focused on RNA secondary structure, has revealed several core principles. One is that the existence of regions within large RNAs with complex, higher-order structure is unremarkable. When these base pairing and tertiary structures affect biological functions, they create “an RNA structure code” with pervasive effects on gene regulatory circuits. Additionally, every RNA likely has a distinct structural personality, which implies that there are numerous ways by which RNA structure tunes the underlying function of an RNA. At the level of secondary structure, such tuning RNA structures tend to function like switches and attenuators that modulate binding by RNA and protein ligands ([ 8 ][9]–[ 11 ][10]). Finally, characterization of well-determined RNA secondary structures often leads to identification of centers of new biology. As it becomes possible to measure, (deeply) learn, and predict the details of the tertiary RNA structure-ome, diverse new discoveries in biological mechanisms await. 1. [↵][11]1. E. J. Strobel et al ., Nat. Rev. Genet. 19, 615 (2018). [OpenUrl][12][CrossRef][13][PubMed][14] 2. [↵][15]1. K. M. Weeks , Acc. Chem. Res. 54, 2502 (2021). [OpenUrl][16][CrossRef][17] 3. [↵][18]1. Z. A. Jaafar, 2. J. S. Kieft , Nat. Rev. Microbiol. 17, 110 (2019). [OpenUrl][19][CrossRef][20] 4. [↵][21]1. R. J. L. Townshend et al ., Science 373, 1047 (2021). [OpenUrl][22][Abstract/FREE Full Text][23] 5. [↵][24]1. J. A. Cruz et al ., RNA 18, 610 (2012). [OpenUrl][25][Abstract/FREE Full Text][26] 6. [↵][27]1. Z. Miao et al ., RNA 26, 982 (2020). [OpenUrl][28][Abstract/FREE Full Text][29] 7. [↵][30]1. E. Pennisi , Science 373, 262 (2021). [OpenUrl][31][Abstract/FREE Full Text][32] 8. [↵][33]1. D. Long et al ., Nat. Struct. Mol. Biol. 14, 287 (2007). [OpenUrl][34][CrossRef][35][PubMed][36][Web of Science][37] 9. 1. M. Kertesz et al ., Nat. Genet. 39, 1278 (2007). [OpenUrl][38][CrossRef][39][PubMed][40][Web of Science][41] 10. 1. D. Dominguez et al ., Mol. Cell 70, 854 (2018). [OpenUrl][42][CrossRef][43][PubMed][44] 11. [↵][45]1. A. M. Mustoe et al ., Biochemistry 57, 3537 (2018). [OpenUrl][46][CrossRef][47] Acknowledgments: The author’s laboratory is supported by the US National Institutes of Health and National Science Foundation. The author is an advisor to and holds equity in Ribometrix. [1]: #ref-1 [2]: #ref-2 [3]: #ref-3 [4]: #ref-4 [5]: #ref-5 [6]: #ref-6 [7]: #ref-7 [8]: pending:yes [9]: #ref-8 [10]: #ref-11 [11]: #xref-ref-1-1 "View reference 1 in text" [12]: {openurl}?query=rft.jtitle%253DNat.%2BRev.%2BGenet.%26rft.volume%253D19%26rft.spage%253D615%26rft_id%253Dinfo%253Adoi%252F10.1038%252Fs41576-018-0034-x%26rft_id%253Dinfo%253Apmid%252F30054568%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/s41576-018-0034-x&link_type=DOI [14]: /lookup/external-ref?access_num=30054568&link_type=MED&atom=%2Fsci%2F373%2F6558%2F964.atom [15]: #xref-ref-2-1 "View reference 2 in text" [16]: {openurl}?query=rft.jtitle%253DAcc.%2BChem.%2BRes.%26rft.volume%253D54%26rft.spage%253D2502%26rft_id%253Dinfo%253Adoi%252F10.1021%252Facs.accounts.1c00118%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]: /lookup/external-ref?access_num=10.1021/acs.accounts.1c00118&link_type=DOI [18]: #xref-ref-3-1 "View reference 3 in text" [19]: {openurl}?query=rft.jtitle%253DNat.%2BRev.%2BMicrobiol.%26rft.volume%253D17%26rft.spage%253D110%26rft_id%253Dinfo%253Adoi%252F10.1038%252Fs41579-018-0117-x%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=10.1038/s41579-018-0117-x&link_type=DOI [21]: #xref-ref-4-1 "View reference 4 in text" [22]: {openurl}?query=rft.jtitle%253DScience%26rft.stitle%253DScience%26rft.aulast%253DTownshend%26rft.auinit1%253DR.%2BJ.%2BL.%26rft.volume%253D373%26rft.issue%253D6558%26rft.spage%253D1047%26rft.epage%253D1051%26rft.atitle%253DGeometric%2Bdeep%2Blearning%2Bof%2BRNA%2Bstructure%26rft_id%253Dinfo%253Adoi%252F10.1126%252Fscience.abe5650%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 [23]: /lookup/ijlink/YTozOntzOjQ6InBhdGgiO3M6MTQ6Ii9sb29rdXAvaWpsaW5rIjtzOjU6InF1ZXJ5IjthOjQ6e3M6ODoibGlua1R5cGUiO3M6NDoiQUJTVCI7czoxMToiam91cm5hbENvZGUiO3M6Mzoic2NpIjtzOjU6InJlc2lkIjtzOjEzOiIzNzMvNjU1OC8xMDQ3IjtzOjQ6ImF0b20iO3M6MjI6Ii9zY2kvMzczLzY1NTgvOTY0LmF0b20iO31zOjg6ImZyYWdtZW50IjtzOjA6IiI7fQ== [24]: #xref-ref-5-1 "View reference 5 in text" [25]: {openurl}?query=rft.jtitle%253DRNA%26rft_id%253Dinfo%253Adoi%252F10.1261%252Frna.031054.111%26rft_id%253Dinfo%253Apmid%252F22361291%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 [26]: /lookup/ijlink/YTozOntzOjQ6InBhdGgiO3M6MTQ6Ii9sb29rdXAvaWpsaW5rIjtzOjU6InF1ZXJ5IjthOjQ6e3M6ODoibGlua1R5cGUiO3M6NDoiQUJTVCI7czoxMToiam91cm5hbENvZGUiO3M6Mzoicm5hIjtzOjU6InJlc2lkIjtzOjg6IjE4LzQvNjEwIjtzOjQ6ImF0b20iO3M6MjI6Ii9zY2kvMzczLzY1NTgvOTY0LmF0b20iO31zOjg6ImZyYWdtZW50IjtzOjA6IiI7fQ== [27]: #xref-ref-6-1 "View reference 6 in text" [28]: {openurl}?query=rft.jtitle%253DRNA%26rft_id%253Dinfo%253Adoi%252F10.1261%252Frna.075341.120%26rft_id%253Dinfo%253Apmid%252F32371455%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 [29]: /lookup/ijlink/YTozOntzOjQ6InBhdGgiO3M6MTQ6Ii9sb29rdXAvaWpsaW5rIjtzOjU6InF1ZXJ5IjthOjQ6e3M6ODoibGlua1R5cGUiO3M6NDoiQUJTVCI7czoxMToiam91cm5hbENvZGUiO3M6Mzoicm5hIjtzOjU6InJlc2lkIjtzOjg6IjI2LzgvOTgyIjtzOjQ6ImF0b20iO3M6MjI6Ii9zY2kvMzczLzY1NTgvOTY0LmF0b20iO31zOjg6ImZyYWdtZW50IjtzOjA6IiI7fQ== [30]: #xref-ref-7-1 "View reference 7 in text" [31]: {openurl}?query=rft.jtitle%253DScience%26rft.stitle%253DScience%26rft.aulast%253DPennisi%26rft.auinit1%253DE.%26rft.volume%253D373%26rft.issue%253D6552%26rft.spage%253D262%26rft.epage%253D263%26rft.atitle%253DProtein%2Bstructure%2Bprediction%2Bnow%2Beasier%252C%2Bfaster%26rft_id%253Dinfo%253Adoi%252F10.1126%252Fscience.373.6552.262%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 [32]: /lookup/ijlink/YTozOntzOjQ6InBhdGgiO3M6MTQ6Ii9sb29rdXAvaWpsaW5rIjtzOjU6InF1ZXJ5IjthOjQ6e3M6ODoibGlua1R5cGUiO3M6NDoiQUJTVCI7czoxMToiam91cm5hbENvZGUiO3M6Mzoic2NpIjtzOjU6InJlc2lkIjtzOjEyOiIzNzMvNjU1Mi8yNjIiO3M6NDoiYXRvbSI7czoyMjoiL3NjaS8zNzMvNjU1OC85NjQuYXRvbSI7fXM6ODoiZnJhZ21lbnQiO3M6MDoiIjt9 [33]: #xref-ref-8-1 "View reference 8 in text" [34]: {openurl}?query=rft.jtitle%253DNature%2Bstructural%2B%2526%2Bmolecular%2Bbiology%26rft.stitle%253DNat%2BStruct%2BMol%2BBiol%26rft.aulast%253DNollmann%26rft.auinit1%253DM.%26rft.volume%253D14%26rft.issue%253D4%26rft.spage%253D287%26rft.epage%253D294%26rft.atitle%253DPotent%2Beffect%2Bof%2Btarget%2Bstructure%2Bon%2BmicroRNA%2Bfunction.%26rft_id%253Dinfo%253Adoi%252F10.1038%252Fnsmb1226%26rft_id%253Dinfo%253Apmid%252F17401373%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 [35]: /lookup/external-ref?access_num=10.1038/nsmb1226&link_type=DOI [36]: /lookup/external-ref?access_num=17401373&link_type=MED&atom=%2Fsci%2F373%2F6558%2F964.atom [37]: /lookup/external-ref?access_num=000245469600014&link_type=ISI [38]: {openurl}?query=rft.jtitle%253DNature%2Bgenetics%26rft.stitle%253DNat%2BGenet%26rft.aulast%253DKertesz%26rft.auinit1%253DM.%26rft.volume%253D39%26rft.issue%253D10%26rft.spage%253D1278%26rft.epage%253D1284%26rft.atitle%253DThe%2Brole%2Bof%2Bsite%2Baccessibility%2Bin%2BmicroRNA%2Btarget%2Brecognition.%26rft_id%253Dinfo%253Adoi%252F10.1038%252Fng2135%26rft_id%253Dinfo%253Apmid%252F17893677%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 [39]: /lookup/external-ref?access_num=10.1038/ng2135&link_type=DOI [40]: /lookup/external-ref?access_num=17893677&link_type=MED&atom=%2Fsci%2F373%2F6558%2F964.atom [41]: /lookup/external-ref?access_num=000249737400026&link_type=ISI [42]: {openurl}?query=rft.jtitle%253DMol.%2BCell%26rft.volume%253D70%26rft.spage%253D854%26rft_id%253Dinfo%253Adoi%252F10.1016%252Fj.molcel.2018.05.001%26rft_id%253Dinfo%253Apmid%252F29883606%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 [43]: /lookup/external-ref?access_num=10.1016/j.molcel.2018.05.001&link_type=DOI [44]: /lookup/external-ref?access_num=29883606&link_type=MED&atom=%2Fsci%2F373%2F6558%2F964.atom [45]: #xref-ref-11-1 "View reference 11 in text" [46]: {openurl}?query=rft.jtitle%253DBiochemistry%26rft.volume%253D57%26rft.spage%253D3537%26rft_id%253Dinfo%253Adoi%252F10.1021%252Facs.biochem.8b00395%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 [47]: /lookup/external-ref?access_num=10.1021/acs.biochem.8b00395&link_type=DOI

New research has found that artificial intelligence (AI) analyzing medical scans can identify the race of patients with an astonishing degree of accuracy, while their human counterparts cannot. With the Food and Drug Administration (FDA) approving more algorithms for medical use, the researchers are concerned that AI could end up perpetuating racial biases. They are especially concerned that they could not figure out precisely how the machine-learning models were able to identify race, even from heavily corrupted and low-resolution images. In the study, published on pre-print service Arxiv, an international team of doctors investigated how deep learning models can detect race from medical images. Using private and public chest scans and self-reported data on race and ethnicity, they first assessed how accurate the algorithms were, before investigating the mechanism.

Spear phishing is a social engineering technique targeted towards a targeted individual to divulge confidential information. But creating highly targeted mass spear-phishing emails could take a lot of effort and time. In a recent test conducted by a team of researchers, it was found that they could use Natural Language Processing (NLP) to devise targeted phishing emails. At the end of the research, the team revealed that AI/ML could be used to develop spear-phishing campaigns at a devastating scale. In the recently held Black Hat Defcon security conference in Las Vegas, a team of researchers hailing from the Singapore Government Technology Agency presented the results of their AI/ML generated phishing email test.

What are the big questions for AI and ethics? If you're preparing to study this area, here are the 20 most important questions to ask. 1. What is AI? - AI (Artificial Intelligence) is a computer system able to perform tasks that normally require human intelligence, such as visual perception, speech recognition, decision making and translation between languages. Artificial intelligence on the other hand refers to programs that exhibit behaviors indistinguishable from those exhibited by humans or animals. It is uncertain what kinds of situations could arise which would have disastrous consequences for humanity and/or the Earth, however there are a variety of scenarios that might lead to these circumstances (some related to military or corporate nature).

In this study, a deep learning bidirectional long short-term memory (BiLSTM) recurrent neural network-based channel state information estimator is proposed for 5G orthogonal frequency-division multiplexing systems. The proposed estimator is a pilot-dependent estimator and follows the online learning approach in the training phase and the offline approach in the practical implementation phase. The estimator does not deal with complete a priori certainty for channels’ statistics and attains superior performance in the presence of a limited number of pilots. A comparative study is conducted using three classification layers that use loss functions: mean absolute error, cross entropy function for kth mutually exclusive classes and sum of squared of the errors. The Adam, RMSProp, SGdm, and Adadelat optimisation algorithms are used to evaluate the performance of the proposed estimator using each classification layer. In terms of symbol error rate and accuracy metrics, the proposed estimator outperforms long short-term memory (LSTM) neural network-based channel state information, least squares and minimum mean square error estimators under different simulation conditions. The computational and training time complexities for deep learning BiLSTM- and LSTM-based estimators are provided. Given that the proposed estimator relies on the deep learning neural network approach, where it can analyse massive data, recognise statistical dependencies and characteristics, develop relationships between features and generalise the accrued knowledge for new datasets that it has not seen before, the approach is promising for any 5G and beyond communication system.

The Transform Technology Summits start October 13th with Low-Code/No Code: Enabling Enterprise Agility. Machine learning is becoming an important tool in many industries and fields of science. But ML research and product development present several challenges that, if not addressed, can steer your project in the wrong direction. In a paper recently published on the arXiv preprint server, Michael Lones, Associate Professor in the School of Mathematical and Computer Sciences, Heriot-Watt University, Edinburgh, provides a list of dos and don'ts for machine learning research. The paper, which Lones describes as "lessons that were learnt whilst doing ML research in academia, and whilst supervising students doing ML research," covers the challenges of different stages of the machine learning research lifecycle. Although aimed at academic researchers, the paper's guidelines are also useful for developers who are creating machine learning models for real-world applications.

This article dives into the tf-explain library. It provides explanations on interpretability methods, such as Grad CAM, with Tensorflow 2.0.

Blockchain artificial intelligence lab Fetch.ai has launched a new NFT marketplace for AI-generated art, giving users the ability to create digital collectibles in a collaborative setting through machine learning technology. The new platform, dubbed Colearn Paint, allows groups of creators to automatically generate and collectively own NFTs designed by a machine learning algorithm. The platform is geared towards "abstract compositions," according to Humayun Sheikh, CEO of Fetch.ai, who cited "collective learning" as a major trend for the future. Collective learning is a concept within artificial intelligence that describes the application of deep learning algorithms to data and privacy. Users of Colearn Paint will be taken through a three-step process for creating randomly generated NFTs.

Computer Vision (CV) is one of the most exciting subfields within the Artificial Intelligence (AI) and Machine Learning (ML) domain. It is a major component for many modern AI/ML pipelines, and it's transforming almost every industry, enabling organizations to revolutionize the way machines and business systems work. Academically, CV has been a well-established area of computer science for many decades, and over the years, a lot of research has gone into this field to make it better. However, the use of deep neural networks has recently revolutionized the field and given it new fuel for accelerated growth. In this article, we discuss some of the most popular and effective datasets used in the domain of Deep Learning (DL) to train state-of-the-art ML systems for CV tasks.