If you are looking for an answer to the question What is Artificial Intelligence? and you only have a minute, then here's the definition the Association for the Advancement of Artificial Intelligence offers on its home page: "the scientific understanding of the mechanisms underlying thought and intelligent behavior and their embodiment in machines."
However, if you are fortunate enough to have more than a minute, then please get ready to embark upon an exciting journey exploring AI (but beware, it could last a lifetime) …
Feature engineering is the process of using domain knowledge of the data to transform existing features or to create new variables from existing ones, for use in machine learning. Data in its raw format is almost never suitable for use to train machine learning algorithms. Instead, data scientists devote a substantial amount of time to pre-process the variables to use them in machine learning. As you can see, feature engineering is an umbrella term that includes multiple techniques to perform everything from filling missing values, to encoding categorical variables, to variable transformation, to creating new variables from existing ones. In this post, I highlight the main feature engineering techniques to process the data and leave it ready to use for machine learning. I describe what each technique entails, and say a few words about when we should use each technique.
At the beginning of the artificial intelligence (AI)/machine learning (ML) era, the expectations are high, and experts foresee that AI/ML shows potential for diagnosing, managing and treating a wide variety of medical conditions. However, the obstacles for implementation of AI/ML in daily clinical practice are numerous, especially regarding the regulation of these technologies. Therefore, we provide an insight into the currently available AI/ML-based medical devices and algorithms that have been approved by the US Food & Drugs Administration (FDA). We aimed to raise awareness of the importance of regulatory bodies, clearly stating whether a medical device is AI/ML based or not. Cross-checking and validating all approvals, we identified 64 AI/ML based, FDA approved medical devices and algorithms. Out of those, only 29 (45%) mentioned any AI/ML-related expressions in the official FDA announcement. The majority (85.9%) was approved by the FDA with a 510(k) clearance, while 8 (12.5%) received de novo pathway clearance and one (1.6%) premarket approval (PMA) clearance. Most of these technologies, notably 30 (46.9%), 16 (25.0%), and 10 (15.6%) were developed for the fields of Radiology, Cardiology and Internal Medicine/General Practice respectively. We have launched the first comprehensive and open access database of strictly AI/ML-based medical technologies that have been approved by the FDA. The database will be constantly updated.
VMware makes software that helps businesses get more work out of data center servers by slicing physical machines into "virtual" ones so that more applications can be packed onto each physical machine. Its tools are commonly used by large businesses that operate their own data centers as well as businesses that use cloud computing data centers. For many years, much of VMware's work focused on making software work better with processors from Intel Corp, which had a dominant market share of data centers. In recent years, as businesses have turned to AI for everything from speech recognition to recognizing patterns in financial data, Nvidia's market share in data centers has been expanding because its chips are used to speed up such work. VMware's software tools will work smoothly with Nvidia's chips to run AI applications without "any kind of specialized setup," Krish Prasad, head of VMware's cloud platform business unit, said during a press briefing.
It's a serious competitor and has made massive gains, but China's AI prowess is still often oversold. Our data suggest that America still leads in AI venture capital and other forms of private-market AI investment, and Chinese investors don't seem to be co-opting American AI startups in large numbers. Policymakers should focus on reinforcing the vibrant, open innovation ecosystem that fuels America's AI advantage, and take a deep breath before acting against China's technology transfer efforts and AI abuses. Action is necessary, but misunderstanding China's overall position in AI could lead to rushed or overbroad policies that do more harm than good. AI is a global wave, not a bipolar contest.
There is no surprise that artificial intelligence is taking the world by storm. The technology is increasingly being used across diverse business functions and revolutionizing all aspects of life and work. AI enables computers to learn from a voluminous amount of data to perform menial and complex tasks. Its applications have been of great value for both an organization or an individual, assisting them in doing their work with ease and getting things done on time. As AI is different from rule-based automation solutions and uses machine learning and NLP, this tech is expected to be as important for humans as electricity and the internet.
During COVID-19, artificial intelligence (AI) has been used to enhance diagnostic efforts, deliver medical supplies and even assess risk factors from blood tests. Now, artificial intelligence is being used to forecast future COVID-19 cases. Texas A&M University researchers, led by Dr. Ali Mostafavi, have developed a powerful deep-learning computational model that uses artificial intelligence and existing big data related to population activities and mobility to help predict the future spread of COVID-19 cases at a county level. The researchers published their results in IEEE Access. The spread of pandemics is influenced by complex relationships related to features including mobility, population activities and sociodemographic characteristics. However, typical mathematical epidemiological models only account for a small subset of relevant features.
It's a great time to be a deep learning engineer. In this article, we will go through some of the popular deep learning frameworks like Tensorflow and CNTK so you can choose which one is best for your project. Deep Learning is a branch of Machine Learning. Though machine learning has various algorithms, the most powerful are neural networks. Deep learning is the technique of building complex multi-layered neural networks.
In the United States, more than half of all households are expected to have a digital assistant or smart speaker like Google Home or Amazon Echo by 2022, and many people already today use these devices for shopping. In the Nordic region, however, relatively few consumers have purchased or plan to purchase an AI-based digital assistant. Those Nordic residents who do have one primarily use assistants to play music, do research and manage to-do lists. Yet when it comes to online shopping, the purchasing journey is to a high degree driven by convenience. In the next few years, AI solutions that save customers time and energy are expected to become increasingly common.
The world is going digital at a pace faster than the blink of an eye. Artificial intelligence (AI) and machine learning (ML) have been heralded as a means of digital technology that can solve a wide range of problems in different industries and applications. This also includes the realm of cybersecurity. Capgemini's Reinventing Cybersecurity with Artificial Intelligence Report, which was published last year, found that 61% of enterprises say they cannot detect breach attempts today without using AI technologies. In a similar survey by Webroot, it was observed that 89% of IT professionals believe their company could be doing more to defend against cyberattacks.
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