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) …
We study revenue optimization pricing algorithms for repeated posted-price auctions where a seller interacts with a single strategic buyer that holds a fixed private valuation. When the participants non-equally discount their cumulative utilities, we show that the optimal constant pricing (which offers the Myerson price) is no longer optimal. In the case of more patient seller, we propose a novel multidimensional optimization functional --- a generalization of the one used to determine Myerson's price. This functional allows to find the optimal algorithm and to boost revenue of the optimal static pricing by an efficient low-dimensional approximation. Numerical experiments are provided to support our results.
In recent years, there has been a greater demand on retailers to adapt to rapid changes in consumer attitudes and the marketplace in order to keep up. Customer experience is very much dependant on retail operations, which have the potential to make or break a customer's journey. It's imperative for retailers to adopt a globally competitive business model, and using artificial intelligence technologies can break down barriers and make it easier to communicate with customers. It's now become possible for businesses to continuously scrutinise customer behaviour data and generate alerts through the power of machine learning. Most companies are already rich with data.
It is difficult to open an insurance industry newsletter these days without seeing some reference to machine learning or its cousin artificial intelligence and how they will revolutionize the industry. Yet according to Willis Towers Watson's recently released 2019/2020 P&C Insurance Advanced Analytics Survey results, fewer companies have adopted machine learning and artificial intelligence than had planned to do so just two years ago (see the graphic below). In the context of insurance, we're not talking about self-driving cars (though these may have important implications for insurance) or chess-playing computers. We're talking about predicting the outcome of comparatively simple future events: Who will buy what product, which clients are more likely to have what kind of claim, which claim will become complex according to some definition. Analytics have applications across the insurance value chain, from marketing, client acquisition and retention to underwriting, pricing and claims management, as insurers look to squeeze more signal out of their data.
Microsoft today announced that Power Automate's robotic process automation (RPA) feature UI flows will hit general availability on April 2. The company also detailed Power Automate's support of attended and unattended RPA scenarios, as well as their pricing. At Ignite 2019 in November, Microsoft renamed its IFTTT competitor Microsoft Flow as Power Automate to align with its Power Platform, a business tool that lets anyone analyze, act, and automate across their organization. That's when the company added UI flows to Power Automate in public preview. RPA is a form of business process automation that relies on bots or AI workers. It's supposed to eliminate repetitive tasks so humans can do what they do best.
Be it smoother business operations or enhancing customer experience, Malay Kant Barik, GM-IT of Shriram General Insurance is leaving no stone unturned when it comes to deploying the latest technologies. While the majority of insurance customers relied on face-to-face interaction for a better understanding of products and pricing model. A rapidly growing segment of digitally aware customers has helped Shriram General Insurance expand its reach and make processes quicker. "We have an AI-based service bot which keeps on learning itself as it interacts with the customers. Implementing this bot has increased our customer service efficiency and capability. At the same time, it continuously offers insights about customer behaviour and how to enhance the customer experience", says Barik.
Standard game-theoretic formulations for settings like contextual pricing and security games assume that agents act in accordance with a specific behavioral model. In practice however, some agents may not prescribe to the dominant behavioral model or may act in ways that are arbitrarily inconsistent. Existing algorithms heavily depend on the model being (approximately) accurate for all agents and have poor performance in the presence of even a few such arbitrarily irrational agents. How do we design learning algorithms that are robust to the presence of arbitrarily irrational agents? We address this question for a number of canonical game-theoretic applications by designing a robust algorithm for the fundamental problem of multidimensional binary search. The performance of our algorithm degrades gracefully with the number of corrupted rounds, which correspond to irrational agents and need not be known in advance. As binary search is the key primitive in algorithms for contextual pricing, Stackelberg Security Games, and other game-theoretic applications, we immediately obtain robust algorithms for these settings. Our techniques draw inspiration from learning theory, game theory, high-dimensional geometry, and convex analysis, and may be of independent algorithmic interest.
A next generation AI-Analytics decisioning solution should be able to tell retailers precisely how their business is doing across various departments and metrics far better than traditional business intelligence solutions can do. More importantly, the AI platform can explain why something happened (causation), and what you should do about it. Traditional business intelligence can't do this! What are examples of what can be done better with predictive and prescriptive AI-analytics capabilities? AI Demand Forecasting can provide highly accurate day-of-week and time-of-day forecasting at an item/store level.
You can find the files for this project at my GitHub and the slides here. The final project is accessible here (interactive web app).] I recently designed a new approach to automatic pricing for Airbnb listings using the Inside Airbnb dataset. I used linear regression to establish a base price and time series analysis to forecast price fluctuations due to the date. I used unsupervised learning to build a recommender system so hosts could compare their listing to other similar popular listings.
American-style options are used not only by traditional asset managers but also by energy companies to hedge "optimised assets" by finding optimal decisions to optimise their P&L and find their value. A common modelling of a power plant unit P&L is done using swing options which are American options allowing to exercise at most l times the option with possibly a constraint on the delay between two exercise dates (see Carmona and Touzi (2008) or Warin (2012) for gas storage modelling). Formally, for T 0, we are given a stochastic processes ( X t) t 0 defined on a probability space (Ω, F, F ( F t) t 0, P) and one wants to find an increasing sequence of F stopping times τ ( τ 1,τ 2,...,τ l) that maximises the expectation of some objective function f E Pnull l null i 1f ( τ i,X τ i) 1 τ i Tnull . Numerical methods to solve the optimal stopping problem when l 1,f ( x,t) e rt g (x) and X is Markovian include: - Dynamic programming equation: the option price P 0 is computed using the following backward discrete scheme over a grid t 0 0 t 1 ... t N T: P t N g ( X T), P t i max( g ( X t i),e r (t i 1 t i) E P( P t i 1 F t i)), i 0,...,N 1 . One then needs to perform regression to compute the conditional expectations, see Longstaff and Schwartz (2001) or Bouchard and Warin (2012).
Back in in 1959, Arthur Samuel coined the term Machine Learning with a purpose. He wanted the computer systems to learn from data without being programmed. This latest approach not only helps the world perform computing processes in an efficient and cost-effective manner but also helps manage the gamut of data-driven affairs. Machine learning starts and sparks with the generic algorithms. It does mining, compiling, analyzing massive data and way beyond.