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) …
It should be clear by now that automation and artificial intelligence are about to hit the enterprise in a big way. But while this transformation will be rapid, it will not happen across the entire IT stack all at once. So where will this new computing paradigm first make its presence known in the enterprise? According to leading researchers, the most obvious candidates are the productivity applications like Enterprise Resource Planning (ERP) and Customer Relations Management (CRM) that have already subsumed much of the IT operational model, and this will effectively create a data environment that will, for the most part, manage itself in response to changing workload requirements. Gartner's David Cearley, for example, notes that artificial intelligence and machine learning have already shown a marked propensity to understand, learn, predict and adapt to a wide range of events, to the point that they can function autonomously even in complex environments.
Work will continue to evolve. And progress is impossible without change. The industrial revolution required routine skills. Most of them are no longer suficient to thrive and do better in the current information revoultion. Today, any field including, legal, finance, biotech, infotech, nanotech, energy, healthcare, education etc. is wide open to revolutionary transformational developments.
Since 2008, a lot of people have looked very unfavorably on purely mathematical economic theory. In a nutshell, Sannikov's theories describe how an employer would adjust a contractor's compensation in real time, in response to changing performance and external conditions. Picture supply chains managed by algorithms, with purchasing contracts adjusted at high frequencies in response to constant flows of data about sales, shipping times, manufacturing costs, and so on. Instead of high-frequency traders, imagine high-frequency lawyers, adjusting contracts to reward contractors appropriately for tiny improvements in efficiency, using sophisticated statistical analysis to figure out whether performance is being driven by outside conditions or by the contractor's skill and effort.
Unstructured data presents challenges for those who are in charge of data management and require enterprise systems that can control the data and access it as needed. It is hard to categorize and store unstructured data vis-à-vis structured data which can be easily quantified and stored in databases. In addition, most HR automation systems perform mere keyword searches. It is imperative for companies to make their HR automation systems more intelligent and help them increase measurable factors such as revenue per employee, costs and time.
In the 2030s there won't be many things AI can't do better than human beings, and that's just 15 years away. One day as society is more automated, human beings won't be asked to do whatever they can find just to put food on the table. The universal basic income (UBI) will be given to all citizens, unconditionally, yes. A world with a universal basic income, means a fairer world.
Marketing automation platforms save time, improve efficiency and increase productivity; but, they do not provide deep insight into the 2.5 quintillion bytes of data being created every day as people move from screen to screen consuming information and making buying decisions. In November 2013, IBM introduced the Watson Ecosystem Program, opening up Watson as a development platform and giving companies the ability to build applications powered by Watson's cognitive computing intelligence. Watson is a cognitive technology that processes information more like a human than a computer -- by understanding natural language, generating hypotheses based on evidence, and learning as it goes. Rather than simply automating manual tasks, artificial intelligence adds a cognitive layer that infinitely expands marketers' ability to process data, identify patterns, and build intelligent strategies and content faster, cheaper and more effectively than humans.
How will these technologies feed machine learning and allow machine design, control systems, production, maintenance and business practices to improve? "Machine learning will help machine builders, integrators and end users by allowing the machines to solve the problems that typically can only be done by humans and, in some cases, can't even be done by humans," says Matt Wicks, vice president, product development, manufacturing systems for Intelligrated, a provider of automated, intelligent conveyance and robotic handling systems in Mason, Ohio. "One thing you might want to take a look at is the Google Trends comparing the search volumes for machine learning vs. artificial intelligence vs. neural networks vs. late-comer deep learning," notes Michael Risse, vice president and CMO at Seeq. "There might be other terms to consider--prescriptive analytics, for example--and then there are the process-industry-specific analytics tools such as advanced process control (APC), statistical process control (SPC), multivariate analysis and even application performance management (APM)," says Risse.
The fact that robotics and automation are crossing price, performance, and adoption thresholds is a clear indication that the robotic megatrend is growing in relevance and a tipping point may be near. But another factor may be fuelling demand – analysts expect the workforce growth rate to decline worldwide, and China, Germany, Japan, and South Korea will be particularly affected. In fact, as robotic technologies advance and their potential to affect more and more industries increases, the main factors holding back future adoption rates may be the concerns of politicians, the public, labour unions, and regulatory agencies, as well as human comfort levels at having robots drive our cars, care for our parents, and displace current workers. In 2000 the company unveiled ASIMO (the acronym for Advanced Step in Innovative Mobility), a sophisticated humanlike robot whose wide range of motion allows it to run and climb stairs.