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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.


AI, RPA, and Machine Learning - How are they Similar & Different?

#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.


Genetic Algorithm (GA) Introduction with Example Code

#artificialintelligence

This tutorial will be diving into genetic algorithms in detail and explaining their implementation in Python. We will also explore the different methods involved in each step diagrammatically. As always, we are including code for reproducibility purposes. We have split the code when required while exploring the different steps involved during our implementation. Make sure to check the full implementation from this tutorial on either Google Colab or Github.


How Machine Learning Will Eliminate Failures In Clinical Trials

#artificialintelligence

One of the hottest topics in drug development is the use of AI in clinical trials. AI is defined as a science concerned with building smart machines capable of performing tasks that typically require human intelligence. AI's full potential will not be realized for at least another decade, but machine learning (a component of AI) is a technology that is available now. Still, judging by the amount of content and webinars I see on this topic, it seems many in the industry are still trying to understand how it will be used and why it may be a game changer when it comes to trial conduct. If you have a small amount of data from two variables, it is easy to plug that information into a spreadsheet, graph it, and look at the relationship between the variables.


Ethics of AI: Benefits and risks of artificial intelligence

ZDNet

In 1949, at the dawn of the computer age, the French philosopher Gabriel Marcel warned of the danger of naively applying technology to solve life's problems. Life, Marcel wrote in Being and Having, cannot be fixed the way you fix a flat tire. Any fix, any technique, is itself a product of that same problematic world, and is therefore problematic, and compromised. Marcel's admonition is often summarized in a single memorable phrase: "Life is not a problem to be solved, but a mystery to be lived." Despite that warning, seventy years later, artificial intelligence is the most powerful expression yet of humans' urge to solve or improve upon human life with computers. But what are these computer systems? As Marcel would have urged, one must ask where they come from, whether they embody the very problems they would purport to solve. Ethics in AI is essentially questioning, constantly investigating, and never taking for granted the technologies that are being rapidly imposed upon human life. That questioning is made all the more urgent because of scale. AI systems are reaching tremendous size in terms of the compute power they require, and the data they consume. And their prevalence in society, both in the scale of their deployment and the level of responsibility they assume, dwarfs the presence of computing in the PC and Internet eras. At the same time, increasing scale means many aspects of the technology, especially in its deep learning form, escape the comprehension of even the most experienced practitioners. Ethical concerns range from the esoteric, such as who is the author of an AI-created work of art; to the very real and very disturbing matter of surveillance in the hands of military authorities who can use the tools with impunity to capture and kill their fellow citizens. Somewhere in the questioning is a sliver of hope that with the right guidance, AI can help solve some of the world's biggest problems. The same technology that may propel bias can reveal bias in hiring decisions. The same technology that is a power hog can potentially contribute answers to slow or even reverse global warming. The risks of AI at the present moment arguably outweigh the benefits, but the potential benefits are large and worth pursuing. As Margaret Mitchell, formerly co-lead of Ethical AI at Google, has elegantly encapsulated, the key question is, "what could AI do to bring about a better society?" Mitchell's question would be interesting on any given day, but it comes within a context that has added urgency to the discussion. Mitchell's words come from a letter she wrote and posted on Google Drive following the departure of her co-lead, Timnit Gebru, in December.


Top 6 AI-Powered Drug Discovery Tools In 2021

#artificialintelligence

Life sciences have benefitted immensely from advances in artificial intelligence. AI has a lot of potential to enhance and accelerate drug discovery -- the process of identifying potential medicines. In January 2020, British start-up Exscientia and Japanese pharmaceutical firm Sumitomo Dainippon Pharma used AI to develop a drug for OCD. The typical drug development processes take around five years to reach the trial stage, but this drug took only a year. Cheminformatics has grown by leaps and bounds in the last decade.


Ethics of AI: Benefits and risks of artificial intelligence

#artificialintelligence

In 1949, at the dawn of the computer age, the French philosopher Gabriel Marcel warned of the danger of naively applying technology to solve life's problems. Life, Marcel wrote in Being and Having, cannot be fixed the way you fix a flat tire. Any fix, any technique, is itself a product of that same problematic world, and is therefore problematic, and compromised. Marcel's admonition is often summarized in a single memorable phrase: "Life is not a problem to be solved, but a mystery to be lived." Despite that warning, seventy years later, artificial intelligence is the most powerful expression yet of humans' urge to solve or improve upon human life with computers. But what are these computer systems? As Marcel would have urged, one must ask where they come from, whether they embody the very problems they would purport to solve. Ethics in AI is essentially questioning, constantly investigating, and never taking for granted the technologies that are being rapidly imposed upon human life. That questioning is made all the more urgent because of scale. AI systems are reaching tremendous size in terms of the compute power they require, and the data they consume. And their prevalence in society, both in the scale of their deployment and the level of responsibility they assume, dwarfs the presence of computing in the PC and Internet eras. At the same time, increasing scale means many aspects of the technology, especially in its deep learning form, escape the comprehension of even the most experienced practitioners. Ethical concerns range from the esoteric, such as who is the author of an AI-created work of art; to the very real and very disturbing matter of surveillance in the hands of military authorities who can use the tools with impunity to capture and kill their fellow citizens. Somewhere in the questioning is a sliver of hope that with the right guidance, AI can help solve some of the world's biggest problems. The same technology that may propel bias can reveal bias in hiring decisions. The same technology that is a power hog can potentially contribute answers to slow or even reverse global warming. The risks of AI at the present moment arguably outweigh the benefits, but the potential benefits are large and worth pursuing. As Margaret Mitchell, formerly co-lead of Ethical AI at Google, has elegantly encapsulated, the key question is, "what could AI do to bring about a better society?" Mitchell's question would be interesting on any given day, but it comes within a context that has added urgency to the discussion. Mitchell's words come from a letter she wrote and posted on Google Drive following the departure of her co-lead, Timnit Gebru, in December.


AI predicts effective drug combinations to fight complex diseases faster

#artificialintelligence

Finding new ways to repurpose or combine existing drugs has proved to be a powerful tool to treat complex diseases. Drugs used to treat one type of cancer, for instance, have effectively strengthened treatments for other cancer cells. Complex malignant tumors often require a combination of drugs, or "drug cocktails," to formulate a concerted attack on multiple cell types. Drug cocktails can not only help stave off drug resistance but also minimize harmful side effects. But finding an effective combination of existing drugs at the right dose is extremely challenging, partly because there are near-infinite possibilities.


How Verge Genomics Uses AI To Tackle Parkinson's, Alzheimer's And ALS

#artificialintelligence

Verge Genomics is taking a novel approach to speed drug discovery for devastating neurodegenerative diseases such as Parkinson's, Alzheimer's and ALS. Rather than spending exhaustive time with animal testing, the San Francisco-based company goes straight to the source. "To succeed in humans, you need to start with humans," says CEO Alice Zhang, whose drug discovery combines artificial intelligence with human genomics. Animal testing can be successful in predicting success for some drugs, but when it comes to neurodegenerative diseases, human brains are far more complex. To keep human patients at the center of the drug discovery process, Zhang's company has built one of the largest databases of brain tissue sequences in the world, with tissue from more than 1,000 human brains.