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Artificial Intelligence at Johnson & Johnson - Current Investments

#artificialintelligence

We see evidence dating back to 2017 that Johnson & Johnson has been regularly publishing about their investments and initiatives related to artificial intelligence. At present, Johnson & Johnson does not seem to boast any mature, deployed applications with the firm itself, but its AI-related investment initiatives indicate their aspirations. According to an analysis by FiercePharma, Johnson & Johnson (J&J) is the largest pharmaceutical firm by revenue, bringing in $82.1 billion in 2019. However, its pharma group has seen the lions' share of J&J's success, outperforming its other units with notable sales expansion in oncology and immunology. J&J claims to be investing in data science competency throughout the firm.


The future is analog: startup InfinityQ pushes novel quantum computer

ZDNet

Startup InfinityQ Technologies is changing the paradigm for quantum computing, says co-founder and CEO Aurélie Hélouis. People often fixate on quantum computing as a hardware technology, a new kind of transistor, but it may make sense to be less literal, less clinical, and a bit more broad-minded. That is the premise of a computer startup called InfinityQ Technologies that came out of stealth mode April 29th, promising to do quantum computing with all of its benefits but without the tedium of a sub-zero refrigerator stuffed full of unstable materials. The key is taking the analog of the qubit, finding a way to do qubits in hardware that is a lot less exotic than typical quantum hardware. "We can create artificial atoms that make possible circuits that behave like a quantum system," said InfinityQ's Aurélie Hélouis in an interview with ZDNet via Zoom.


How Knowledge Graphs Will Transform Data Management And Business

#artificialintelligence

In late November the U.S. Federal Drug Administration approved Benevolent AI's recommended arthritis drug Baricitnib as a COVID-19 treatment, just nine-months after the hypothesis was developed. The correlation between the properties of this existing Eli Lilly drug and a potential treatment for seriously ill COVID-19 patients, was made with the help of knowledge graphs, which represent data in context, in a manner that humans and machines can readily understand. Knowledge graphs apply semantics to give context and relationships to data, providing a framework for data integration, unification, analytics and sharing. Think of them as a flexible means of discovering facts and relationships between people, processes, applications and data, in ways that give companies new insights into their businesses, create new services and improve R&D research. Benevolent AI, a six-year-old London-based company which has developed a platform of computational and experimental technologies and processes that can draw on vast quantities of biomedical data to advance drug development, built-in the use of knowledge graphs from day one.


Autonomous Driving, AI System on a Chip, Drug Discovery Firms Among Top Funded - AI Trends

#artificialintelligence

The top-funded companies on the recently-released list of top 100 most-promising AI companies to watch from CBInsights, a market intelligence company based in New York, include companies offering autonomous driving software, an AI System on a chip, endpoint security with AI, and a drug discovery company. The list, selected from a base of 6,000 companies, is based on business relations, investor profile, news sentiment analysis, R&D activity, a proprietary scoring system, market potential, competitive landscape, team strength and tech novelty, according to an account in TechRepublic. "This year's cohort spans 18 industries, and is working on everything from climate risk to accelerating drug R&D," stated CB Insights CEO Anand Sanwal. Companies on last year's list went on to raise $5.2 billion in additional financing, including 16 of over $100 million each. Some companies exited via merger or acquisition, IPOs or SPACS.


Embracing AI When Your Industry Is in Flux

#artificialintelligence

One of the great challenges we have seen businesses face in recent years is how they approach data and analytics (and now artificial intelligence) when their industries are undergoing major transformation. It's hard enough to create a data-driven culture, compete on analytics, develop data-driven products and services, and so forth under normal business conditions, as we noted in our March column about the newest NewVantage Partners survey on big data and AI. But doing it while your business and industry are transforming -- the old line of changing out a jet engine while the plane is flying through turbulence at 35,000 feet -- is really tough. It's so difficult, in fact, that we always have our doubts when executives claim to have done it successfully. We are much more trusting when we're told that the organization is simply making progress toward the goal.


Artificial Intelligence, a Major Factor Behind Pfizer's US$900M Profit

#artificialintelligence

Pfizer has been on the headlines quite often recently. The Covid-19 vaccine is what made the company atop other field competitors. Remarkably, Pfizer has also yielded the benefit of US$900 million in the first quarter of 2021, thanks to its vaccine production and distribution programs. But behind the vaccine making and circulation, disruptive technologies played a big role in finding the correct drug and helped the company in its trials. Pfizer has effectively used artificial intelligence to conduct vaccine trials and streamline the distribution.


How To Discover Antiviral Drugs With Deep Learning?

#artificialintelligence

Drug discovery is a time-consuming and expensive process; deep learning can make this process faster and cheaper. Drug discovery can be divided into three parts. Machine learning problems are broadly divided into three subgroups: supervised learning, unsupervised learning, and reinforcement learning. Drug characteristics prediction can be stated as a supervised learning problem. Drug discovery is an unsupervised learning process. Data collection: First of all, we need information on successful antiviral drugs.


Illuminating the first bacteria

Science

The ability to sequence genes and, more recently, whole genomes has transformed our understanding of the tree of life by elucidating the tremendous diversity of microorganisms and by placing plants, animals, and fungi as branches nested among microbial lineages ([ 1 ][1]–[ 3 ][2]). The resulting evolutionary tree divides life into three domains: the exclusively microbial Bacteria and Archaea, and Eukarya, organisms whose cells contain nuclei (including ciliates, amoebae, and animals). Yet, the ordering of the earliest branching events on the tree and the nature of now-extinct ancestors remains unclear. On page 588 of this issue, Coleman et al. ([ 4 ][3]) provide a new estimate of the root of the bacterial tree of life, that is, the ancestor from which all bacterial species are derived. Knowledge of the root of the bacterial tree is important because it defines the evolutionary starting point for the tremendous diversity of Bacteria and offers glimpses into the nature of the first bacterial cells. ![Figure][4] The root of the bacterial tree of life Coleman et al. infer that the root of the bacterial tree of life lies between “Gracilicutes” and “Terrabacteria,” which enables reconstruction of the last bacterial common ancestor (LBCA). Unresolved are two clades, “DST” (Deinococcota, Synergistota, and Thermotogota) and Fusobacteriota (Fuso), that were the recipients of a disproportionately large number of lateral gene transfers (LGTs). Names in quotes represent provisional taxonomic terms. GRAPHIC: N. CARY/ SCIENCE Although genome sequencing has given biologists a wealth of data for estimating phylogenies (i.e., evolutionary trees), it has also revealed the importance of nonvertical inheritance because genes, and sometimes whole genomes, have been transferred across species boundaries ([ 2 ][5], [ 3 ][2], [ 5 ][6]). This lateral gene transfer (LGT; also called horizontal gene transfer) has substantially altered bacterial evolution, for example, through the spread of antibiotic resistance and the acquisition of metabolic pathways ([ 2 ][5]). In some lineages, such as the bacterial clade Thermatogales, a large number of LGTs have generated a chimeric lineage: An estimated 20% of the genes in the Thermotoga genus have been acquired from Archaea rather than through vertical transmission during binary division ([ 5 ][6]). Eukaryotes represent another chimera, because they arose through the merger of an archaeon and a bacterium, the latter eventually became a mitochondrion ([ 6 ][7], [ 7 ][8]). Events such as these make estimation of the root of a tree challenging because transferred genes reflect different histories than vertically inherited genes and thus can confound inferences. Coleman et al. use a method developed by their group called amalgamated likelihood estimation (ALE) to estimate the position of the root of the bacterial tree of life while attempting to account for gene duplications and losses as well as LGTs. To determine the root of the bacterial tree, Coleman et al. first construct an unrooted phylogeny based on 62 single-copy genes sampled from 265 bacterial genomes and then use ALE to evaluate various positions of the root on this tree through analyses of the evolutionary history of 11,000 gene families sampled from across the same lineages. Coleman et al. estimate that two-thirds of the transmissions among gene families analyzed are vertical and that the root of the bacterial tree of life likely lies between “Gracilicutes”—predominantly Gram-negative bacteria, including Proteobacteria, Acidobacteria, and Spirochaeta—and “Terrabacteria,” which includes Gram-positive bacteria, Cyanobacteria, Firmicutes, and “CPR” (candidate phyla radiation); taxonomic names in quotes indicate uncertainty (see the figure). The placement of two clades—“DST” (Deinococcota, Synergistota, and Thermotogota) and Fusobacteriota (an anaerobic clade of Gram-negative bacteria)—that are the recipients of a disproportionate number of LGTs is unresolved. This is exemplary of the uncertainty introduced by LGT in reconstructing a bacterial tree of life. Knowing the root of the bacterial tree of life allows both polarization of the evolution of bacterial traits and reconstruction of the last bacterial common ancestor (LBCA), at least for those aspects that have evolved through vertical transmission. Coleman et al. infer that LBCA was a free-living rod-shaped cell surrounded by a double membrane and with a flagellum. The genome of this ancestor of all Bacteria encoded key components of informational processing (e.g., DNA transcription and replication); metabolic pathways, including CO2 fixation and the Krebs cycle; and elements of the CRISPR-Cas9 adaptive immune system. Overall, these inferences agree with findings of others on the physiological and morphological features of LBCA ([ 8 ][9], [ 9 ][10]). Did they get it right? It is difficult to know because reconstructing such ancient events [the earliest fossil bacteria are estimated to have existed around 3 billion to 3.4 billion years ago ([ 10 ][11])] presents a challenge even in the absence of LGT. Results are dependent on the parameters used in the mathematical models, and although Coleman et al. extensively evaluate these parameters through simulations, assumptions about the relative rates of gene duplication and LGT have been questioned ([ 11 ][12]). To further complicate inferences, Coleman et al. note the importance of taxon sampling because changing representative species within major bacterial clades affects the estimate of the position of the root. This suggests that future analyses including different, and perhaps newly discovered, lineages may alter both estimates of the root of the bacterial tree of life and inferences about LBCA. Another confounding factor is the strength of the assumption that Bacteria are monophyletic (that Bacteria descended from a common ancestor that existed more recently than their shared common ancestor with Archaea). This requires either the independent evolution of Bacteria and Archaea at the origin of life ([ 12 ][13]) or a complex pattern of diversification and extinction events to give rise to reciprocally monophyletic Bacteria and Archaea (i.e., the evolution of two distinct lineages of microorganisms). Coleman et al. state that they are agnostic as to the monophyly of Bacteria, pointing out the limitations of previous studies that rely on an archaeal outgroup for rooting the bacterial tree of life. Instead, their model excludes Archaea and the nested eukaryotes ([ 6 ][7], [ 7 ][8]) in their estimates of the bacterial root. Alternative models suggest that Archaea descended from LBCA ([ 13 ][14], [ 14 ][15]), in which case Archaea would have to be included to determine the root of the bacterial tree of life (see the figure). Whereas some will contest the proposed position of the root of the bacterial tree of life, others challenge the concept of a tree as a model for the diversification of life on Earth ([ 14 ][15], [ 15 ][16]). Although Coleman et al. may have accurately captured the vertical portion of the bacterial tree of life, the tree that they root is missing the history of biological innovations and ecological adaptations that are derived from LGTs. To portray these lateral events, some have suggested that the tree of life be replaced with a circle, a web, or a network depicting the complex history of inheritance ([ 2 ][5], [ 3 ][2], [ 5 ][6]). It has been suggested that “to save the trees, one might define organisms as more than the sums of their genes and imagine organismal lineages to have a sort of emergent reality” ([ 2 ][5]). In other words, there is room for alternative methods and innovation to encompass both vertical and lateral inheritance, focusing on the evolutionary history of phenotypic features such as metabolism, morphology, and life history. 1. [↵][17]1. L. A. Hug et al ., Nat. Microbiol. 1, 16048 (2016). [OpenUrl][18] 2. [↵][19]1. W. F. Doolittle , Science 284, 2124 (1999). [OpenUrl][20][Abstract/FREE Full Text][21] 3. [↵][22]1. T. Dagan, 2. W. Martin , Genome Biol. 7, 118 (2006). [OpenUrl][23][CrossRef][24][PubMed][25] 4. [↵][26]1. G. A. Coleman et al ., Science 372, eabe0511 (2021). [OpenUrl][27][Abstract/FREE Full Text][28] 5. [↵][29]1. J. P. Gogarten, 2. J. P. Townsend , Nat. Rev. Microbiol. 3, 679 (2005). [OpenUrl][30][CrossRef][31][PubMed][32][Web of Science][33] 6. [↵][34]1. T. A. Williams et al ., Nature 504, 231 (2013). [OpenUrl][35][CrossRef][36][GeoRef][37][PubMed][38][Web of Science][39] 7. [↵][40]1. A. B. Collens, 2. L. A. Katz , J. Hered. 112, 140 (2021). [OpenUrl][41] 8. [↵][42]1. W. F. Martin , Front. Microbiol. 11, 817 (2020). [OpenUrl][43] 9. [↵][44]1. M. Miyata et al ., Genes Cells 25, 6 (2020). [OpenUrl][45] 10. [↵][46]1. E. J. Javaux , Nature 572, 451 (2019). [OpenUrl][47][CrossRef][48] 11. [↵][49]1. T. J. Treangen, 2. E. P. C. Rocha , PLOS Genet. 7, e1001284 (2011). [OpenUrl][50][CrossRef][51][PubMed][52] 12. [↵][53]1. F. L. Sousa et al ., Philo. Trans. R. Soc. London Ser. B 368, 1 (2013). [OpenUrl][54][CrossRef][55][PubMed][56] 13. [↵][57]1. T. Cavalier-Smith , Int. J. Syst. Evol. Microbiol. 52, 7 (2002). [OpenUrl][58][CrossRef][59][PubMed][60][Web of Science][61] 14. [↵][62]1. J. A. Lake et al ., Philos. Trans. R. Soc. London B Biol. Sci. 364, 2177 (2009). [OpenUrl][63][CrossRef][64][PubMed][65] 15. [↵][66]1. S. Gribaldo, 2. C. Brochier , Res. Microbiol. 160, 513 (2009). [OpenUrl][67][CrossRef][68][PubMed][69][Web of Science][70] Acknowledgments: I thank A. Cote-L'Heureux and several other colleagues for thought-provoking conversations on this topic. L.A.K. is supported by grants from the National Science Foundation (OCE-1924570, DEB-1651908, and DEB-1541511) and National Institutes of Health (R15HG010409). 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How is Novartis Capitalizing on AI for Medical Innovations?

#artificialintelligence

Artificial intelligence is nothing strange now, as it is being used by almost all businesses in different industries. The rapid digital transformation and technology adoption in recent years has led many companies to extensively invest in AI to drive growth. Novartis International AG is a leading global healthcare company, based out of Switzerland, that has been efficiently capitalizing on its AI capabilities to develop medical innovations. Novartis incorporates digital and disruptive technologies to create transformative treatment and drug discoveries. Everybody is trying to adopt AI, but how many focus on an ethical approach?


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#artificialintelligence

AI, RPA, and machine learning, you must have heard these words echoing in the tech industry. Be it blogs, websites, videos, or even product descriptions, disruptive technologies have made their presence bold. The fact that we all have AI-powered devices in our homes is a sign that the technology has come so far. If you are under the impression that AI, robotic process automation, and machine learning have nothing in common, then here's what you need to know, they are all related concepts. Oftentimes, people use these names interchangeably and incorrectly which causes confusion among businesses that are looking for the latest technological solutions.