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) …
In American society, the service sector, which generally produces intangible rather than tangible goods, rules the U.S. economy and accounts for about 80 percent of the nation's gross domestic product. Service is in the eye of the beholder, and Americans have spoken loudly concerning their level of satisfaction with "service" in the U.S. economy. For example, the American Customer Satisfaction Index (ACSI) (comprising a cross-section of U.S. industries), based in the Ross School of Business, University of Michigan-Ann Arbor, tracks quarterly the overall satisfaction level of company performance by U.S. customers (ranking it from a low of 0 to a high of 100). Since recording a survey result of 77.0 in the third quarter of 2018, U.S. customer satisfaction has declined continuously to 73.6 in the first quarter of 2021, a ranking not surveyed by the ACSI since 2005. Interestingly, according to ACSI, "manufactured goods tend to score higher for customer satisfaction than do services.
While the country continues to struggle convincing people it is safe and smart to get vaccinated against Covid-19 despite emergency authorization and advocacy from the U.S. Federal Drug Administration (FDA), other growing numbers of people are willing to jump in with both feet when it comes to trusting their lives to Artificial Intelligence (AI), which has no standards or organizational oversight. At this time, only "guidelines" exist from the U.S. Federal Trade Commission (FTC) for AI. I will take you through an example of how even impactful applications of AI still need a human assistant to ensure trustworthy, explainable, and unbiased decision making. Using AI for automating manufacturing and improving our work streams, such as providing robust CMMS (computerized maintenance management systems) is an excellent application for AI. CMMS is a proactive methodology to keep systems running and to optimize maintenance operations.
SEO.co, a search engine optimization (SEO) agency, in collaboration with DEV.co, a custom software development company, has launched AI.DEV.co, a tool that makes it easier for businesses and individuals to generate web copy. Businesses of all sizes and in all industries face a similar dilemma online: getting attention and standing out. Most brands use a combination of different marketing and advertising strategies to get this attention and differentiate themselves from their competitors. For a campaign to succeed, it needs a set of compelling, unique copy – persuasive writing that concisely makes a point and motivates a web user to take an action (such as clicking a link, buying a product, or watching a video). Generating copy is challenging for several reasons, even if one is experienced in the field.
While a majority of AI projects still don't reach production, the interest for no-code AI platforms keeps rising. Indeed, a growing number of startups and large tech firms now propose "easy-to-use" ML platforms. The idea of being able to build and use a solution based on Machine Learning without being a data scientist is something very interesting for both small and large companies who could empower their employees while dedicating more resources to complex ML projects. In this article, I will share what have I learned after having implemented one of these no-code AI solutions and analyzed several startups related to this industry. As an AI consultant, my goal was to determine if these solutions could help us increase the chance of having more projects transitioning from proof of concepts (PoCs) to scalable, relevant, and efficient deployed AI solutions.
Artificial intelligence (AI) is one of the fastest-growing industries and has become increasingly important in all business sectors. With the recent advances in AI technology, investments in AI are also increasing. More and more startups, companies and large corporations switch to AI to automate and enhance business processes and generate more leads. AI has been at the forefront of technological innovation, and companies who implement AI solutions in their business maintain competitiveness in the market. So if you've decided to invest in AI technology, here are the steps to consider before integrating it into your company: It's important to first identify your business problems AI is most likely to solve.
The research arm of the Big Blue and The Michael J. Fox Foundation (MJFF) have built an AI model that can group typical symptom patterns of Parkinson's disease (PD) and accurately pinpoint the progression of these symptoms in a patient, despite whether or not they are taking medications to mask those symptoms. The discovery, published in The Lancet Digital Health, was one of the key goals the two organisations had aimed to achieve at its outset. IBM Research and MJFF have been working together since July 2018 to examine how machine learning could be applied to help clinicians further understand the underlying biology of PD, particularly as it progresses so differently from individual to individual. As part of developing the AI model, the researchers used de-identified datasets from the Parkinson's Progression Markers Initiative (PPMI). "The dataset served as the input to the machine learning approach, enabling the discovery of complex symptom and progression patterns," IBM Research said in the research paper. "While many previous studies have focused on characterising Parkinson's disease using only baseline information, our method relies on up to seven years of patient data.
This forms the basis for everything else. The central object in Numpy is the Numpy array, on which you can do various operations. We know that the matrix and arrays play an important role in numerical computation and data analysis. Pandas and other ML or AI tools need tabular or array-like data to work efficiently, so using NumPy in Pandas and ML packages can reduce the time and improve the performance of the data computation. NumPy based arrays are 10 to 100 times (even more than 100 times) faster than the Python Lists, hence if you are planning to work as a Data Analyst or Data Scientist or Big Data Engineer with Python, then you must be familiar with the NumPy as it offers a more convenient way to work with Matrix-like objects like Nd-arrays.
Even though the above diagram is a bit of simplification, this is how most ETL workflows may look like. To put simply, ETL is an automated process to move data from source systems to target systems, involving various stages for Extract, Transform and Load sub-processes, without data-loss and while maintaining data-integrity. This also, is usually referred to as data-migration. The objective of ETL is to have a clean, classified, enriched and curated data at one place (data warehouse or data lake). Machine-learning models and analytic tools are run against this data to fetch useful information and predictions, based on which business decisions can be taken.
Researchers at the University of Sydney and quantum control startup Q-CTRL today announced a way to identify sources of error in quantum computers through machine learning, providing hardware developers the ability to pinpoint performance degradation with unprecedented accuracy and accelerate paths to useful quantum computers. A joint scientific paper detailing the research, titled "Quantum Oscillator Noise Spectroscopy via Displaced Cat States," has been published in the Physical Review Letters, the world's premier physical science research journal and flagship publication of the American Physical Society (APS Physics). Focused on reducing errors caused by environmental "noise"--the Achilles' heel of quantum computing--the University of Sydney team developed a technique to detect the tiniest deviations from the precise conditions needed to execute quantum algorithms using trapped ion and superconducting quantum computing hardware. These are the core technologies used by world-leading industrial quantum computing efforts at IBM, Google, Honeywell, IonQ, and others. To pinpoint the source of the measured deviations, Q-CTRL scientists developed a new way to process the measurement results using custom machine-learning algorithms.
SAN MATEO, Calif., July 29, 2021 (GLOBE NEWSWIRE) -- Chooch AI, the leading computer vision AI platform, has been cited for accelerating adoption of computer vision–powered solutions across industry verticals by leading research company IDC. Chooch AI models are ready to deploy now both in the cloud and on edge devices. Clients include Fortune 500 companies and the US Government. Partners include NVIDIA, Intel, Dell, Deloitte, Convergint and Vantiq. IDC states that, "Chooch AI's horizontal- and vertical-agnostic platform supports rapid data set generation capabilities using machine labeling techniques such as smart annotation, data augmentation, and use of synthetic data, along with pretrained ready-to-use models. They believe this will accelerate adoption and time to value computer vision–powered solutions across industry verticals."