It is no doubt that the sub-field of machine learning / artificial intelligence has increasingly gained more popularity in the past couple of years. Some of the most common examples of machine learning are Netflix's algorithms to make movie suggestions based on movies you have watched in the past or Amazon's algorithms that recommend books based on books you have bought before. For me, my first introduction is when I took an Artificial Intelligence class when I was studying abroad in Copenhagen. Machine learning algorithms can be divided into 3 broad categories -- supervised learning, unsupervised learning, and reinforcement learning.Supervised learning is useful in cases where a property (label) is available for a certain dataset (training set), but is missing and needs to be predicted for other instances.
One possible solution may be found in Agent-Based Simulation (ABS), a novel approach to solving complex business problems through computer simulations. One of the most appealing aspects of ABS is that it combines domain expertise and data. The domain expertise is used to define the structure of the simulation, which is unique to each business problem. With this approach, the manager's expertise regains the primary role, and the results of the simulation can be analyzed by the manager and data scientist together.
Take ants, for example: each performs a simple task that helps that hive work as a complex system. Vendors have been talking about emotion measurement for at least the last 5 years, but most of them have been trying to build and deploy monolithic "emotion" measuring systems that work with either complex inputs or "overall" emotion analysis. Swarm intelligence isn't expected to understand all of the special cases: it's just machine learning with a narrow specialization. We know that AI and machine learning will change the world – and swarm intelligence is one of the types of AI that will bring about this change.
Think of a cow as existing in three states: moseying around feeding on grass, standing there staring, and lying down resting. They did it mathematically, building on their earlier work that modeled how a cow moves between its various states. This new study scales that up to explore how multiple cows with their multiple states interact to create crowd dynamics--the so-called emergent properties of cow biology. Welcome to the burgeoning field of complex systems science.
NEW YORK CITY - 15 Jun 2017: LivePerson, Inc. (Nasdaq: LPSN), a leading provider of cloud mobile and online business messaging solutions, and IBM (NYSE: IBM) have announced LiveEngage with Watson, the first global, enterprise-scale, out-of-the-box integration of Watson-powered bots with human agents. The new offering combines IBM's Watson Virtual Agent technology with LivePerson's LiveEngage platform, allowing brands to rapidly and easily deploy conversational bots that get smarter with each interaction, and lets consumers message those brands from their smartphone - via the brand's app, SMS, Facebook Messenger, or even the brand's mobile site - instead of having to call an 800 number. IBM Global Business Services, the company's consulting unit, is providing a set of strategy and implementation services to help companies integrate LiveEngage with Watson as part of their broader business transformation. LivePerson, Inc. (NASDAQ: LPSN) is the leading provider of mobile and online messaging business solutions, enabling a meaningful connection between brands and consumers.
Overall investment in automation technologies – including robotic process automation (RPA), autonomics, virtual customer service agents and personal assistants, natural language processing and machine learning – is expected to double in the next two years, the survey finds, as enterprises look to harness technologies that have the flexibility to solve more than one business problem. "Robotic process automation, autonomic systems and cognitive agents are making employees more productive by taking over routine, process-oriented tasks. Yet, 54 percent say they prefer to buy the business outcomes of automation and AI (cost avoidance, productivity, quality, etc.) The firm specializes in digital transformation services, including automation, cloud and data analytics; sourcing advisory; managed governance and risk services; network carrier services; technology strategy and operations design; change management; market intelligence and technology research and analysis.
Dear readers, the 3rd part of Fintech series focuses on the role of Artificial Intelligence (AI) in the space of Fintech Services.When it comes to fintech, the idea is to create smarter AI that helps finance work better for financial sector workers, investors and anyone who simply wants to figure out the best way to pay their mortgage. By using smart agents that can examine and crunch data about individual behavior and compare to broader datasets, small and big businesses could have the ability to deliver personalized financial services as a scope and scale never possible before. AI can also power technologies that overlay humans to supply worker's activities with a tracking and oversight mechanism, helping with compliance, security, and the observation of employee actions. Possibly, it's an artificially intelligent agent that will help deliver cheaper, private services that are better and faster.
To analyze these clusters, changes in syndromes and consequently the core that remains fundamentally the same, complexity science applies network theory and analysis to explore the underlying structure. Hence, to organize behavior rules to set as base for agent based simulations, Common tools that complexity scientists use are extrapolating network trends from similar risks like extrapolating telematics network for drone insurance, game theory, genetic algorithms, heuristics and cognitive tendencies that we humans apply uncovered by behavioral finance, and neural networks. Agent based modeling combined individual decision and network rules to model policyholder behavior, allowing us to simulate behavior at an individual level and then analyze the overall, aggregate outcomes. The next post will follow a case study of agent based modeling to real life problem of underwriting cycles and highlight how Complexity Science adds value beyond traditional analyses.
Researchers at Microsoft developed an artificial intelligence (AI) algorithm that can achieve the maximum score on Ms. Pac-Man, 999,999, four times greater than the highest human score. The system, according to Microsoft's blog, was developed by a Maluuba, a deep learning startup company which was acquired by Microsoft earlier in the year. The divide-and-conquer method assigns individual AI agents different tasks but also allows them to work together collaboratively through a "top manager." Potential applications include helping a company's sales team make predictions about which customers to target depending on factors such as which clients are up for contract renewal, which contracts are most valuable to the company, and if the customer is available at a particular day or time.
Researchers have created an artificial intelligence-based system that learned how to get the maximum score of 999,990 on the addictive 1980s video game, Ms. Pac-Man The technique, which the team has named'Hybrid Reward Architecture', used 150 agents, which worked in parallel with one another. The researchers found that the best results were achieved when each agent acted egotistically, while the top agent made the best choice for everyone. For example, Mr Mehrotra said the method they could be used to help a company's sales organisation make precise predictions about which potential customers to target at a particular time or on a particular day. For example, Mr Mehrotra said the method they developed to beat Ms. Pac-Man could be used to help a company's sales organisation make precise predictions about which potential customers to target at a particular time or on a particular day.