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) …
This is a very exciting time to be in Manufacturing! Manufacturing Engineers' toolboxes are expanding everyday with new technologies and possibilities for greater efficiency and capability. The Fourth Industrial Revolution, or what some are calling Industry 4.0, is bringing all kinds of tools to bear on all processes in our industrial world. It used to be that only very large companies could take advantage of the latest technologies due to cost, limited availability, and the need for continuous development as they were implemented. Today, the suppliers of these critical technologies have more robust, user friendly solutions at costs that enable even small to medium size enterprises to employ. The next step in embracing this revolution is making sure that Manufacturing Engineers are exposed to and educated on what is possible.
New research from the Global Talent Competitiveness Index 2020 confirms that to succeed in the age of AI, more investment is needed in skills development and lifelong learning. While the emerging markets lag far behind the talent-rich nations, the gap can be bridged with the right set of policies. The currency of the AI-driven economy is talent. But while it is true that talent is high in demand, it is also short in supply. This especially rings true for the economies that fail to attract and build their own talented workforces.
Kremlin analysts could have used Twitter as a source of military intelligence to inform their actions in the 2014 Russia–Ukraine conflict, a study has found. University of California experts showed that location-tagged tweets by Ukraine residents could have been used to map out sentiments towards Russia in real-time. The map they made of pro-Kremlin regions turned out to bear a striking resemblance to the actual areas to which Russia dispatched its special forces. Specifically, this included Crimea and regions in the far east of Ukraine -- where the incoming forces would have been most likely to be seen as liberators. In contrast, the data could also reveal those areas where dispatching forces would have lead to greater resistance and corresponding casualties and costs.
If Sunspring is anything to go by, artificial intelligence in film-making has some way to go. This short film, made as an entry to Sci-Fi London's 48-hour film-making competition in 2016, was written entirely by an AI. The director, Oscar Sharp, fed a few hundred sci-fi screenplays into a long short-term memory recurrent neural network (the type of software behind predictive text in a smartphone), then told it to write its own. The result was almost, but not quite, incoherent nonsense, riddled with cryptic nonsequiturs, bizarre turns of phrase and unfathomable stage directions such as "he is standing in the stars and sitting on the floor". All of which Sharp and his actors filmed with sincere commitment.
Given the current buzz around the whole industry, you could be forgiven for thinking that the whole of artificial intelligence (AI) and Machine Learning sprang magically from out of the oceans of research five years ago, but for those of us who've been providing AI solutions to enterprises for several decades we can but watch recent interest and smile knowingly. However, the appearance of Neural Networks (NN) on the center stage over the same timescale has been little short of phenomenal. Ever since Horace Barlow's pioneering experiments of the 1950s, AI researchers have had a fondness for Neural Networks in the ambitious hope that one day they'd recreate the power of the human brain. But even when I helped create the first version of IDOL Server 20 years ago, Neural Networks were not yet fit-for-purpose, a bit player on the AI scene, slow to train and prone to over-fitting. Then came the Long Short-term Memory improvements in speech-to-text of around ten years ago that started the revolution that has resulted in Neural Networks' powering the wonderfully-spooky sounding field of Deep Learning and finally achieving the recognition that its persistent academic fan base always imagined it would one day receive.
In 1985 the US pulled the plug on a computer-controlled anti-aircraft tank after a series of debacles in which its electronic brain locked guns onto a stand packed with top generals reviewing the device. Mercifully it didn't fire, but did subsequently attack a portable toilet instead of a target drone. The M247 Sergeant York (pictured above) may have been an embarrassing failure, but digital technology and artificial intelligence (AI) have changed the game since then. Today defence contractors around the world are competing to introduce small unmanned tracked vehicles into military service. Just like an army on the move, there are contrasting views about how far and how fast this technology will advance.
Fifty years ago, we couldn't have predicted that tens of thousands of jobs would be created by the development and deployment of driverless cars. Without a doubt, when the first flying cars make their debut in the next few years, thousands of jobs will surface to build, train and support them. If history has taught us anything, it's that innovation and invention keep humanity moving forward. It's within our DNA to evolve and improve our ways of living and working. But what about when it comes to the power of technology?
There is a definition of a Fourth Industrial Revolution (4IR) as the age of digitalization. This is making smart cities, vastly improved factories and a lot of automation of tasks and services in our homes and at work. Industry 4.0 enables real-time data gathering, analysis, and decision- and prediction-making capabilities. Nextbigfuture would indicate that this 4IR is just an extension of the third industrial revolution of computers and robotic automation. The adoption levels of computers and robots are too low and the impact on factory and production levels has not reached the level of improvements reached by the Ford factories and oil machinery over the steam age.
Creating the accelerated, highly-personalized online service customers demand and quickly developing new products in response to shifting regulatory and market forces won't be possible unless insurance and wealth management companies can fully leverage tomorrow's technology. Taking that leap into the tech-enabled future won't be easy for most companies, whose infrastructure is decades old, but the potential revolutions in risk mitigation, customer experience, and product development are so significant that modernization is not going to be optional. Emerging technologies like the Internet of Things (IoT) and Artificial Intelligence (AI) will reshape all industries. They will cause customer expectations to continue to rise as products and experiences become more personalized. The insurance and wealth management companies that can't meet those heightened expectations will be left behind.
The future of work will be shaped by intelligent automation technologies such as robots, AI, machine learning, and more. Most organizations will need these tools to keep up with a rapid pace of change and level of uncertainty not seen since the Industrial Revolution. For technology leaders, there's a lot riding on your next steps: Act too slow, and risk falling behind; act too quickly, and generate unnecessary complexity and confusion. This is where a visionary CIO can step into a leadership position and direct the organization's technology path forward. Download Forrester's guide to the Future of Work to understand the latest technology trends and decide where your organization should focus its efforts.