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 tools are rapidly changing how financial institutions operate, manage data, and interact with customers. The revolution brought by the AI – a blend of three advanced technologies: machine learning, natural language processing and cognitive computing – has huge implications for the financial services industry in Nigeria. According to Microsoft Nigeria Country Manager, Mr Akin Banuso, with the use of modern tools like Microsoft's Azure Machine Learning platform, Financial Service Providers can crunch large volumes of data faster and more accurately, which considerably lessens time-to-market to deliver products and services. "The AI has the potential to advance nearly every field of human endeavour and address countless societal challenges. This is why we are investing in not only making the technology more accessible, but also building capacity in the use of machine learning concepts to address analytical gaps in financial inclusion and other areas," Banuso says.
In many respects, we are reinventing modern programming tools for the A.I. age. Models and expensive resources like talent, data and computing power are currently centralized within large tech corporations. TensorFlow, Tensorflow Hub, AutoML, Algorithmia, and cloud computing are all examples of increasing decentralization of artificial intelligence. Accelerate development (1000 brains are better than 100). Make A.I. safer (more people involved to check and balance development).
Although science fiction may depict AI robots as the bad guys, some tech giants now employ them for security. Companies like Microsoft and Uber use Knightscope K5 robots to patrol parking lots and large outdoor areas to predict and prevent crime. The robots can read license plates, report suspicious activity and collect data to report to their owners. These AI-driven robots are just one example of "autonomous things," one of the Gartner Top 10 strategic technologies for 2019 with the potential to drive significant disruption and deliver opportunity over the next five years. "The future will be characterized by smart devices delivering increasingly insightful digital services everywhere," said David Cearley, Gartner vice president and Fellow, at Gartner 2018 Symposium/ITxpo in Orlando, Florida.
Gartner, Inc. today highlighted the top strategic technology trends that organizations need to explore in 2019. Analysts presented their findings during Gartner Symposium/ITxpo, which is taking place here through Thursday. Gartner defines a strategic technology trend as one with substantial disruptive potential that is beginning to break out of an emerging state into broader impact and use, or which are rapidly growing trends with a high degree of volatility reaching tipping points over the next five years. "The Intelligent Digital Mesh has been a consistent theme for the past two years and continues as a major driver through 2019. Trends under each of these three themes are a key ingredient in driving a continuous innovation process as part of a ContinuousNEXT strategy," said David Cearley, vice president and Gartner Fellow.
The Massachusetts Institute of Technology is pumping $1 billion into a new center for the study of the "global opportunities and challenges presented by the prevalence of computing and the rise of artificial intelligence," the school said Monday. In a statement, MIT said the MIT Stephen A. Schwarzman College of Computing will open in September 2019 as "an interdisciplinary hub for work in computer science, AI, data science, and related fields." A $350 million foundational gift from Schwarzman, head of the massive investment firm Blackstone, will get the project rolling. In addition to Schwarzman's gift, MIT has raised another $300 million for the college that bears his name, with further fundraising being "actively pursued" to raise the $1 billion needed for the learning hub, the statement said. "The College's attention to ethics matters enormously to me, because we will never realize the full potential of these advancements unless they are guided by a shared understanding of their moral implications for society," Schwarzman said in the release.
This is an eclectic collection of interesting blog posts, software announcements and data applications I've noted over the past month or so. ONNX Model Zoo is now available, providing a library of pre-trained state-of-the-art models in deep learning in the ONNX format. In the 2018 IEEE Spectrum Top Programming Language rankings, Python takes the top spot and R ranks #7. Julia 1.0 has been released, marking the stabilization of the scientific computing language and promising forwards compatibility. Google announces Cloud AutoML, a beta service to train vision, text categorization, or language translation models from provided data.
Science has always relied on a combination of approaches to derive an answer or develop a theory. The seeds for Darwin's theory of natural selection grew under a Herculean aggregation of observation, data, and experiment. The more recent confirmation of gravitational waves by the Laser Interferometer Gravitational-Wave Observatory (LIGO) was a decades-long interplay of theory, experiment, and computation. Certainly, this idea was not lost on the U.S. Department of Energy's (DOE) Argonne National Laboratory, which has helped advance the boundaries of high-performance computing technologies through the Argonne Leadership Computing Facility (ALCF). Realizing the promise of exascale computing, the ALCF is developing the framework by which to harness this immense computing power to an advanced combination of simulation, data analysis, and machine learning.
When Facebook and Twitter were born, a new era of social media was ushered in, opening the gates for new areas of expertise that hadn't existed before. At first, we all grappled to establish the culture together, but fast forward a decade and it is literally a science with thousands of supporting technology companies. So as Artificial Intelligence (AI) takes over marketing, doesn't that mean it will replace marketers? If you can ask your smart speaker in your office what your engagement growth increase was for your Facebook Page, and ask for recommendations of growth, how do marketing professionals survive? Marketers will survive the same way they did as social media was introduced – the practice will evolve and new niches will be born.
To step into the world of Python for Data Science, you don't need to know Python like your own kid. Just the basics will be enough. If you haven't yet started with Python, we suggest you read An Introduction to Python. To gear up with Python for Data Science, we suggest Anaconda. It is a freemium open source distribution of the Python and R programming languages for large-scale data processing, predictive analytics, and scientific computing.
And it won't replace your radiologist. That stated, I agree with Curtis Langlotz, MD, PhD of Stanford, who stated at RSNA this year that radiologists who use AI will replace radiologists who don't. So, what is the path toward making AI a key enabler for medicine? AI-powered healthcare requires three key factors: sound data science, sharp focus and strategic deployment. And, it requires the patience to balance the excitement of advanced digital technology with the practical realities of how healthcare operates.