When medical historians write about the coronavirus pandemic, they'll likely focus on the slow U.S. response and failures of leadership that led to a tragically high death toll. But that will be only part of the story. From the wreckage and devastation will emerge something few contemporary observers would expect: a brighter future for American healthcare. Five technologies, all previously underappreciated and underutilized, will help our nation move past the coronavirus crisis into a new, golden era of medicine. Like the seedlings of the eucalyptus tree, which sprout only after a forest fire, these technological solutions will blossom in the aftermath of the Covid-19 pandemic--turning U.S. healthcare's outdated and broken system into one that is more convenient, effective and affordable.
Last week, the U.S. Food and Drug Administration presented the organization's first Artificial Intelligence/Machine Learning (AI/ML)- Based Software as a Medical Device (SaMD) Action Plan. This plan portrays a multi-pronged way to deal with the Agency's oversight of AI/ML-based medical software. The Artificial Intelligence/Machine Learning (AI/ML)- Based Software as a Medical Device (SaMD) Action Plan is a response to stakeholder input on the FDA's 2019 regulatory structure for AI and ML-based medical items. FDA additionally will hold a public workshop on algorithm transparency and draw in its stakeholders and partners on other key activities, for example, assessing predisposition in algorithms. While the Action Plan proposes a guide for propelling a regulatory framework, an operational structure gives off an impression of being further down the road.
I am a recent graduate of the Galvanize Data Science Immersive Bootcamp. In this Data Science Bootcamp we spent 3 months learning Statistics, Linear Algebra, Calculus, Machine Learning, SQL, and Python Programming. The San Francisco based program I attended was transferred from in-person to remote due to the COVID-19 pandemic. To say this experience was challenging would be an understatement. My official day at the Bootcamp started at 8:30 AM and ended at 8:30 PM Monday through Friday.
As someone who has interviewed with several companies for Data Scientist positions, as well as someone who has searched and explored countless required qualifications for interviews, I have compiled my top five Data Science qualifications. These qualifications are not only expected to be required by the time of interview, but also just important qualifications to keep in mind at your current work, even if you are not interviewing. Data Science is always evolving so it is critical to be aware of new technologies within the field. These requirements may differ from your personal experiences, so keep in mind this article is stemming from my opinion as a professional Data Scientist. These qualifications will be described as key skills, concepts, and various experiences that are expected to have before entering the new role or current role.
Alan Kalton, Vice President and General Manager of Aktana Europe, is a leader in data analytics and manages all new Contextual Intelligence implementations and developments across Europe. He comes to Aktana from Cape Town, South Africa where he led a data analytics venture called BroadReach and prior was the Analytics Leader of EY in South Africa. He also held prominent executive leadership positions in data analytics at IBM, Elsevier, Cognizant, Steris, Novartis, GSK, and ZS Associates. He graduated with a BS and MSc of industrial and operations engineering from the University of Michigan. Kalton can be reached at firstname.lastname@example.org.
We basically train machines so as to include some kind of automation in it. In machine learning, we use various kinds of algorithms to allow machines to learn the relationships within the data provided and make predictions using them. So, the kind of model prediction where we need the predicted output is a continuous numerical value, it is called a regression problem. Regression analysis convolves around simple algorithms, which are often used in finance, investing, and others, and establishes the relationship between a single dependent variable dependent on several independent ones. For example, predicting house price or salary of an employee, etc are the most common regression problems.
Cybersecurity is set to be one of the areas most impacted by artificial intelligence (AI) technologies, with both organizations and cyber criminals deploying AI in their own ways. As AI increases the risk and effectiveness of cyber attacks, organizations must also increase their efforts. The future outcomes of such attacks will greatly depend on who has a better grasp of AI technologies. As we become more of a digital world, the risk of AI-powered cyber attacks also dramatically increases. AI and machine learning are not only used by IT security professionals, but they are deployed by state-sponsored actors, criminal cyber organizations, and individuals.
Naive Bayes is a classification algorithm that works based on the Bayes theorem. Before explaining about Naive Bayes, first, we should discuss Bayes Theorem. Bayes theorem is used to find the probability of a hypothesis with given evidence. In this, using Bayes theorem we can find the probability of A, given that B occurred. A is the hypothesis and B is the evidence.
Anewly designed artificial intelligence tool based on the structure of the brain has identified a molecule capable of wiping out a number of antibiotic-resistant strains of bacteria, according to a study published on February 20 in Cell. The molecule, halicin, which had previously been investigated as a potential treatment for diabetes, demonstrated activity against Mycobacterium tuberculosis, the causative agent of tuberculosis, and several other hard-to-treat microbes. The discovery comes at a time when novel antibiotics are becoming increasingly difficult to find, reports STAT, and when drug-resistant bacteria are a growing global threat. The Interagency Coordination Group (IACG) on Antimicrobial Resistance convened by United Nations a few years ago released a report in 2019 estimating that drug-resistant diseases could result in 10 million deaths per year by 2050. Despite the urgency in the search for new antibiotics, a lack of financial incentives has caused pharmaceutical companies to scale back their research, according to STAT. "I do think this platform will very directly reduce the cost involved in the discovery phase of antibiotic development," coauthor James Collins of MIT tells STAT.
With all the hype over Artificial Intelligence, there is additionally a lot of disturbing buzz about the negative results of AI. More than one-quarter (27%) of all employees state they are stressed that the work they have now will be disposed of within the next five years because of new innovation, robots or artificial intelligence, as indicated by the quarterly CNBC/SurveyMonkey Workplace Happiness review. In certain industries where technology already has played a profoundly disruptive role, employees fear of automation likewise run higher than the normal: Workers in automotives, business logistics and support, marketing and advertising, and retail are proportionately more stressed over new technology replacing their jobs than those in different industries. The dread stems from the fact that the business is already witnessing it. Self-driving trucks already are compromising the jobs of truck drivers, and it is causing a huge frenzy in this job line.