Once the realm of science fiction, artificial intelligence is making inroads into many aspects of healthcare, with one recent report from UnivDatos predicting the technology will attain a market value of $26.6 billion by 2025. Some may fear AI has entered a Wild West phase, unfettered by proper policies and oversight. A report issued by the U.S. Government Accountability Office (GAO), in concert with the National Academy of Medicine (NAM), signals that the government and healthcare professionals are taking a closer look at the AI phenomenon, and that regulatory oversight may emerge down the road. Artificial Intelligence in Health Care: Benefits and Challenges of Technologies to Augment Patient Care, a 106-page report to "congressional requesters," addresses the promise of AI in healthcare, as well as the need to exercise caution. In a jointly signed introductory letter to the report, Karen L. Howard, PhD, director, science, technology assessment, and analytics for the GAO and J. Michael McGinnis, MD, MA, MPP, the Leonard D. Schaeffer Executive Officer and Executive Director of the NAM Leadership Consortium, said "AI has promising applications in health care, including in augmenting patient care. For example," they continued, "it may have the potential to improve treatment, reduce burden on providers, and generally increase the efficiency with which health care facilities and providers use resources, resulting in potential cost savings or health gains. However, as might be expected with a tool with such broad potential use in health and health care decision-making, applying AI tools for health and health care also raises ethical, legal, economic, and social questions."

The Pentagon's Joint Artificial Intelligence Center has awarded a $93.3 million contract to General Atomics Aeronautical Systems Inc (GA-ASI), makers of the MQ-9 Reaper, to equip the drone with new AI technology. The aim is for the Reaper to be able to carry out autonomous flight, decide where to direct its battery of sensors, and to recognize objects on the ground. The contract, announced at the end of last month, builds on a successful test earlier this year. In some ways this is not a major development, more of an incremental step using existing technology. What makes it significant is the drone that is being equipped, and what it will be able to do afterwards.

Peter Hotez deceives New York Times readers about the odds of dying from measles in the US to persuade parents to comply with the CDC's vaccine schedule. On January 9, the New York Times published an article written by Dr. Peter J. Hotez titled "You Are Unvaccinated and Got Sick. His purpose in writing is to persuade parents to vaccinate their children according to the routine schedule recommended by the Centers for Disease Control and Prevention (CDC). To that end, he purports to compare "the dangerous effects of three diseases with the minimal side effects of their corresponding vaccines." "To state it bluntly," Hotez writes, "being unvaccinated can result in illness or death. Vaccines, in contrast, are extremely unlikely to lead to side effects, even minor ones like fainting." He laments that "vaccination rates have fallen", resulting in a resurgence of measles globally. He cites the example of Samoa, where "almost 5,700 measles cases have been recorded since September, resulting in at least 83 deaths. Almost all of those who died were young children." In the US, he writes, "vaccine hesitancy is contributing to" measles outbreaks. Hotez presents data ostensibly to enable parents "to compare the risks of becoming ill with measles . . . to the minute chances of experiencing side effects from their corresponding vaccines." Hotez goes on to assert, "Moreover, new research reveals that, even when patients recover, the measles virus can suppress the immune system, rendering children susceptible to serious infections like pneumonia and the flu." "misinformation spread after an article implying a link between measles vaccinations and autism was published in The Lancet in 1998; it was retracted in 2010 over concerns about the validity of the results and the conduct of the study.

The Pentagon's Joint Artificial Intelligence Center has awarded a $93.3 million contract to General Atomics Aeronautical Systems Inc (GA-ASI), makers of the MQ-9 Reaper, to equip the drone with new AI technology. The aim is for the Reaper to be able to carry out autonomous flight, decide where to direct its battery of sensors, and to recognize objects on the ground. The contract, announced at the end of last month, builds on a successful test earlier this year. In some ways this is not a major development, more of an incremental step using existing technology. What makes it significant is the drone that is being equipped, and what it will be able to do afterwards.

Israeli MedTech companies have experienced quite the acceleration process following the COVID-19 outbreak, with digital health companies garnering the bulk of the focus. The AI medical train has left the station with companies like Ibex Medical, Aidoc, Scopio, and more all scoring funding and coveted FDA approvals this year. However, in the land of medical-AI FDA clearances, one horse stands alone, or should I say Zebra. Israeli startup Zebra Medical Vision, the deep-learning medical imaging analytics company, announces today its seventh FDA clearance. The newly approved solution utilizes the power of AI to extract bone measurements from X-ray scans, similar in quality to CT scans, for the purpose of planning orthopedic surgical procedures.

The cybersecurity industry is trending upwards. More people are sharing sensitive data online, which entices criminals to find new ways to attack internet users. Every industry faces change, but the change the cybersecurity industry is facing may threaten job security. What Change is Threatening Job Security? A couple of trends are doing this.

Some people are interested to Python but might still have questions in their mind, are there any big companies who uses Python programming. They are concern whether is it worth it to learn Python? Python is the fastest growing programming language on the planet. It is also the most wanted programming language by developers in 2017, 2018, and 2019. Aside from its simplicity, Python is well-known as a multi paradigm programming language. Programmers are freely to develop their programs using different approaches, including object-oriented programming, functional programming or procedural programming. This makes so many tech companies love Python and use it to their real world applications.

General Atomics Aeronautical Systems, Inc. (GA-ASI) has demonstrated the DARPA-developed Collaborative Operations in Denied Environment (CODE) autonomy engine on the company's Avenger Unmanned Aircraft System (UAS). CODE was used in order to gain further understanding of cognitive Artificial Intelligence (AI) processing on larger UAS platforms for air-to-air targeting. Using a network-enabled Tactical Targeting Network Technology (TTNT) radio for mesh network mission communications, GA-ASI was able to demonstrate integration of emerging Advanced Tactical Data Links (ATDL), as well as separation between flight and mission critical systems. During the autonomous flight, CODE software controlled the manoeuvring of the Avenger UAS for over two hours without human pilot input. GA-ASI extended the base software behavioural functions for a coordinated air-to-air search with up to six aircraft, using five virtual aircraft for the purposes of the demonstration.

Existing studies on question answering on knowledge bases (KBQA) mainly operate with the standard i.i.d assumption, i.e., training distribution over questions is the same as the test distribution. However, i.i.d may be neither reasonably achievable nor desirable on large-scale KBs because 1) true user distribution is hard to capture and 2) randomly sample training examples from the enormous space would be highly data-inefficient. Instead, we suggest that KBQA models should have three levels of built-in generalization: i.i.d, compositional, and zero-shot. To facilitate the development of KBQA models with stronger generalization, we construct and release a new large-scale, high-quality dataset with 64,331 questions, GrailQA, and provide evaluation settings for all three levels of generalization. In addition, we propose a novel BERT-based KBQA model. The combination of our dataset and model enables us to thoroughly examine and demonstrate, for the first time, the key role of pre-trained contextual embeddings like BERT in the generalization of KBQA.