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) …
Artificial intelligence helps lead decisions over intelligent automation. Intelligent automation can be thought of as a combination of "robotic process automation and artificial intelligence," according to an article on the topic in HR Dive. HR Dive is a publication designed for human resources professionals. Organizations that embrace intelligent automation may experience a return on investment of 200% or more, according to an Everest Group report cited by HR Dive. However, that doesn't mean organizations can automatically anticipate a reduction in headcount.
WIRE)--NTT Research, Inc., a division of NTT (TYO:9432), today announced that a research scientist in its Physics & Informatics (PHI) Lab, Dr. Hidenori Tanaka, was the lead author on a technical paper that advances basic understanding of biological neural networks in the brain through artificial neural networks. Titled "From deep learning to mechanistic understanding in neuroscience: the structure of retinal prediction," the paper was presented at NeurIPS 2019, a leading machine-learning, artificial intelligence (AI) and computational neuroscience conference, and published in Advances in Neural Information Processing Systems 32 (NIPS 2019). Work on the paper originated at Stanford University, academic home of the paper's six authors when the research was performed. At the time, a post-doctoral fellow and visiting scholar at Stanford University, Dr. Tanaka joined NTT Research in December 2019. The underlying research aligns with the PHI Lab's mission to rethink the computer by drawing inspirations from computational principles of neural networks in the brain.
Airbus SE is using artificial intelligence to squeeze cost out of its finance function, an experiment launched in the aircraft maker's Americas division that could save the corporation millions of dollars annually if rolled out in other regions. It's one of the latest examples of how companies across sectors are digitizing operations to increase efficiency, reduce human error and free up employees for tasks that require more human judgment, such as strategic planning, analysis and audits. "Companies can now automate highly repetitive activity at a lower cost with a higher degree of accuracy," said David Axson, head of the CFO consulting practice at Accenture Strategy, a unit of consulting firm Accenture PLC. "This especially applies to high-volume-use cases like accounts payable." Less than half of companies' accounts-payable activity worldwide is currently automated, Accenture Strategy says.
Fiix, a Toronto-based asset management software provider, announced the launch of Fiix Foresight, the first and only AI engine for maintenance. Foresight fuses the benefits of industrial AI-- data capture, pattern detection, analysis, and real-time insights-- with Fiix's market-leading maintenance platform into an easy-to-use, no coding required system purpose-built to help maintenance teams proactively detect problems, identify opportunities for improvement and make quick, data-based decisions. "Industry 4.0 technology like software, sensors, and mobile apps are generating piles of data, which has only increased over the past few months as COVID-19 accelerates digitization across the board. But the companies that are going to thrive now are those that can put that data to work and Fiix is answering that call," says James Novak, CEO of Fiix. The launch follows an incredibly strong June for Fiix, with the company seeing significant momentum both in customer and revenue growth as maintenance teams globally look to modernize their operations.
In recent years, there has been a surge in demand for AI-driven big data analysis in various business fields. AI is also expected to help support the detection of anomalies in data to reveal things like unauthorized attempts to access networks, or abnormalities in medical data for thyroid values or arrhythmia data. Data used in many business operations is high-dimensional data. As the number of dimensions of data increases, the complexity of calculations required to accurately characterize the data increases exponentially, a phenomenon widely known as the "Curse of Dimensionality"(1). In recent years, a method of reducing the dimensions of input data using deep learning has been identified as a promising candidate for helping to avoid this problem. However, since the number of dimensions is reduced without considering the data distribution and probability of occurrence after the reduction, the characteristics of the data have not been accurately captured, and the recognition accuracy of the AI is limited and misjudgment can occur (Figure 1). Solving these problems and accurately acquiring the distribution and probability of high-dimensional data remain important issues in the AI field.
Decision trees are simple to implement and equally easy to interpret. And decision trees are idea for machine learning newcomers as well! If you are unsure about even one of these questions, you've come to the right place! Decision Tree is a powerful machine learning algorithm that also serves as the building block for other widely used and complicated machine learning algorithms like Random Forest, XGBoost, and LightGBM. You can imagine why it's important to learn about this topic!
Rezo is a new-age smart ticketing solution, supporting customization to suit internal business cases like reading from Kinesis, triggering dynamic code. At the same time, Rezo is able to provide us the barebones functionality that comes with the most popular ticketing tools available commercially. Their development turnaround time is in days vs months for most existing solutions. We are extremely happy to be using Rezo and have implemented 10 processes to drive productivity with smart dispatch. In the face of volatile, accelerated change and rising customer demands, new-age internet companies like us have an increased focus on maintaining & improving current levels of performance.
By now, it's almost old news that artificial intelligence (AI) will have a transformative role in medicine. Algorithms have the potential to work tirelessly, at faster rates and now with potentially greater accuracy than clinicians. In 2016, it was predicted that'machine learning will displace much of the work of radiologists and anatomical pathologists'. In the same year, a University of Toronto professor controversially announced that'we should stop training radiologists now'. But is it really the beginning of the end for some medical specialties?
Principal Component Analysis (PCA) is a great tool for a data analysis projects for a lot of reasons. If you have never heard of PCA, in simple words it does a linear transformation of your features using covariance or correlation. I will add a few links below if you want to know more about it. Some of the applications of PCA are dimensional reduction, feature analysis, data compression, anomaly detection, clustering and many more. The first time I learnt about PCA, it was not easy to understand and quite confusing.
Matrix Factorization (MF) (e.g., Probabilistic Matrix Factorization and NonNegative Matrix Factorization) techniques have become the crux of many real-world scenarios, including graph representation and recommendation system (RecSys) because they are powerful models to find the hidden properties behind the data. The idea behind matrix factorization is to represent users and items in a lower-dimensional latent space. And is widely used in the recommendation system and dimensionality reduction. Although, there are many Python libraries that could perform matrix factorization, building the algorithm from scratch could be helpful to understand the basics. Also, there are many complex cases when the matrix factorization library could not handle.