The drug discovery ecosystem is changing rapidly. The rise of robotics and AI enables the emergence of a new model of data-driven drug discovery. Bringing together recent advances in life sciences automation and machine learning applications for drug discovery, new partnerships evolve that allow for game-changing improvements in the drug discovery process. The webinar will provide an overview on large-scale data and metadata capture enabled by end-to-end automation, going beyond what is currently possible in traditional wet lab operations, and will present case studies showing the impact on biotech and pharma operations, providing actionable insights for biopharma leaders. Disclaimer Regarding Audio/Video Recording: a) By participating in this Webinar, you will be participating in an event where photography, video and audio recording may occur. b) By participating in this webinar, you consent to interview(s), photography, audio recording, video recording and its/their release, publication, exhibition, or reproduction to be used for news, web casts, promotional purposes, telecasts, advertising, inclusion on web sites, or for any other purpose(s) that Invitrocue, its vendors, partners, affiliates and/or representatives deems fit to use. You release Invitrocue, its employees, and each and all persons involved from any liability connected with the taking, recording, digitising, or publication of interviews, photographs, computer images, video and/or or sound recordings.
Preface: This is the transcript of my TEDx 2020 talk. It was a great learning experience about how to present a lot of ideas and impact in a very compact form. Your body is a very complex machine, and when there's a problem with it, it could be due to several causes. And, even after a cause has been determined, in the United States, the patients' access to their doctors may be limited by the rarity of their disease, their health insurance and socioeconomic status, or their geographic area. I believe that it does not have to be that way.
Most of you are probably familiar with the chip giants like Intel & AMD which command a bigger share of the computing processor market, but this entrant to the chip market in 1993 has solidified its reputation as a big name in the arena. Although most well-known for its graphical processing units (GPUs) -- GeForce is its primary & most popular product line, the company also provides system-on-a-chip units (SoCs) for the mobile computing and automotive market. Since 2014, Nvidia has begun to diversify its business from the niche markets of gaming, automotive electronics, and mobile devices. It is now venturing into the futuristic AI, along with providing parallel processing capabilities to researchers and scientists that allow them to efficiently run high-performance applications. Let's review of some these endeavors.
Will Thursday 13 August 2020 be remembered as a pivotal moment in democracy's relationship with digital technology? Because of the coronavirus outbreak, A-level and GCSE examinations had to be cancelled, leaving education authorities with a choice: give the kids the grades that had been predicted by their teachers, or use an algorithm. They went with the latter. The outcome was that more than one-third of results in England (35.6%) were downgraded by one grade from the mark issued by teachers. This meant that a lot of pupils didn't get the grades they needed to get to their university of choice.
In our next Fireside Chat, Natalie Yeadon will sit down with Dr. Andree Bates, Founder & CEO of Eularis, to discuss how Eularis helps healthcare teams utilize AI and "Future Tech" to solve their biggest commercial challenges and deliver measurable growth. Among many other interesting topics, they will dissect the barriers to the adoption of "Future Tech"; their predictions for the roles that AI will play in healthcare, Pharma, and medical research within the next few years; and how AI be leveraged to improve patient-centricity. Don't miss out on this exciting Fireside Chat.
Artificial intelligence, including machine and deep learning, are revolutionizing the drug discovery and development process – bringing with it unprecedented levels of speed and efficiency. Meanwhile, San Francisco's unparalleled tech ecosystem provides Bay Area biotechs proximity to myriad opportunities for AI integration. Join us to explore the latest technology and business trends in this burgeoning sector through the lens of some of San Francisco's top life science leaders and rising stars, and learn how AI is impacting investment and partnerships.
COVID-19 impact will also be included and considered for forecast. Global Artificial Intelligence in Market research report provides detail information about Market Introduction, Market Summary, Global market Revenue (Revenue USD), Market Drivers, Market Restraints, Market Opportunities, Competitive Analysis, Regional and Country Level. Global Artificial Intelligence in Marketing market is valued at USD 6.99 Billion in 2018 and expected to reach USD 37.08 Billion by 2025 with the CAGR of 26.98% over the forecast period. Growing adoption of customer-centric marketing strategies and increased use of social media for advertising are some of the factors which are expected to drive the growth of Global Artificial Intelligence in Marketing Market. Artificial intelligence (AI) in marketing is the process of utilizing data models, mathematics and algorithms to generate insights that can be used by marketers. Marketers will use AI-derived insights to guide future decisions about campaign spending, strategy and content topics.
New technologies like 5G and edge computing are making healthcare more connected, secure, and efficient. When healthcare practitioners must make life-or-death decisions, the quality of information at their disposal is critical. Having more specific data -- and being able to access it in real time -- leads to more informed decisions. The Internet of Medical Things (IoMT) makes this possible through an infrastructure of connected medical devices, software applications, and health systems powered by 5G wireless technology and edge computing, which enables connected devices to process data closer to where it is created. Global healthcare funding to private companies reached a new quarterly record of $18.1B in Q2'20. Get the report to learn more.
Based out of Singapore, Gero develops new drugs for ageing and other complicated disorders using its proprietary developed artificial intelligence (AI) platform. Recently, the company has secured $2.2 million (€1.9 million) in Series A funding, bringing the total capital raised since Gero's founding to over $7.5 million (€6.4 million). Gero's founder Peter Fedichev, said, "We are happy with the recognition and support from these strategic investors who themselves are acknowledged leaders in the fields of AI and biotechnology. This will help us attain the necessary knowledge at the junction of biological sciences and AI/ML technologies that is necessary for the radical acceleration of drug discovery battling the toughest medical challenges of the 21st century. We hope that the technology will soon lead to a meaningful healthspan extension and quality of life improvements " The round was led by Bulba Ventures with participation from previous investors and serial entrepreneurs in the fields of pharmaceuticals, IT, and AI.
Anomalies are occurrences in a dataset that are in some way unusual and do not fit the general patterns. The concept of the anomaly is generally ill-defined and perceived as vague and domain-dependent. Moreover, no comprehensive and concrete overviews of the different types of anomalies have hitherto been published. By means of an extensive literature review this study therefore offers the first theoretically principled and domain-independent typology of data anomalies, and presents a full overview of anomaly types and subtypes. To concretely define the concept of the anomaly and its different manifestations the typology employs four dimensions: data type, cardinality of relationship, data structure and data distribution. These fundamental and data-centric dimensions naturally yield 3 broad groups, 9 basic types and 61 subtypes of anomalies. The typology facilitates the evaluation of the functional capabilities of anomaly detection algorithms, contributes to explainable data science, and provides insights into relevant topics such as local versus global anomalies.