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) …
Martin Welker is the chief executive of Axonic. Even with the support of AI frameworks like TensorFlow or OpenAI, artificial intelligence still requires deep knowledge and understanding compared to a mainstream web developer. If you have built a working prototype, you are probably the smartest guy in the room. Congratulations, you are a member of a very exclusive club. With Kaggle, you can even earn decent money by solving real-world projects.
Humanity is now developing our greatest contribution to the expansion of intelligence on the planet: the flowering of artificial intelligence. It would be a shame if all we used it for were Amazon shopping and Facebook birthday reminders. Luckily, machine learning and artificial intelligence aren't just a for-profit undertaking. Universities, companies, nonprofits, and governmental agencies are already busy developing interesting tools and applications that direct machine learning toward the common good. Though still in their early days, these initiatives just may represent our best bet for addressing our most challenging ecological and societal problems.
The first 500 Emirati men and women will soon start their training in artificial intelligence as the UAE looks to begin shaping its future in a field that is expected to soon touch every company and individual on the planet. The students' training is part of an agreement signed by Oracle and the Higher Colleges of Technology on Sunday to prepare young UAE nationals youth for the country's future jobs. Oracle is also discussing with the university ways to develop AI-related degrees. "We're focusing on research and development in technology as well as providing the necessary support and training for local youth," said Omar Al Olama, Minister of Artificial Intelligence, at the launch in Dubai. "Most importantly, we are focusing on utilising emerging technologies in public services to enhance day-to-day experiences of UAE citizens and increase the efficiency of the government and private sectors.
Frost & Sullivan describes robotic process automation (RPA) as software that incorporates technologies such as artificial intelligence (AI) and machine learning (ML) to automate routine, high-volume tasks that are sensitive to human error. While RPA can boost business efficiencies and ROI without increasing costs, it should not be seen as a replacement for existing business process management (BPM) systems. That's the view of Nancy Jamison, Frost & Sullivan's Digital Transformation Principal Analyst. Jamison who was commenting on the company's recently published whitepaper Robotic Process Automation: A New Era of Agent Engagement, which examined the increasingly important role RPA is playing in contact centres. Across all industries, RPA acts as hidden glue that ties together many business processes, with RPA workforces improving organisational efficiency by offloading live resources, improving accuracy, maintaining compliance, and reducing costs.
Jeff Heepke knows where to plant corn on his 4,500-acre farm in Illinois because of artificial intelligence (AI). He uses a smartphone app called Climate Basic, which divides Heepke's farmland (and, in fact, the entire continental U.S.) into plots that are 10 meters square. The app draws on local temperature and erosion records, expected precipitation, soil quality, and other agricultural data to determine how to maximize yields for each plot. If a rainy cold front is expected to pass by, Heepke knows which areas to avoid watering or irrigating that afternoon. As the U.S. Department of Agriculture noted, this use of artificial intelligence across the industry has produced the largest crops in the country's history.
The IEEE publishes an annual list of the Top 10 Technology Trends for each upcoming year. Making the list for 2018 are multiple topics surrounding artificial intelligence and machine learning. Deep learning comes in as the IEEE hottest trend for 2018. Neural networks extract features through a concept of layers. By combining the output from these multiple layers, deeper layers are able to construct more advanced insight from data.
Insurers and brokers are already using these technologies, to varying degrees. But it could be the best and fastest adopters of these technologies – those companies that dive in the deepest – who come out on top as the industry continues to rapidly evolve. According to Deloitte's "2018 Insurance Industry Outlook", robotics and artificial intelligence (AI) in insurance is only going to increase in the next few years. Insurer spending on cognitive intelligence (CI) and AI technologies will rise 48% globally, "on a compound annual growth basis over five years," to reach $1.4 billion by 2021. It's sink or swim time for insurers adopting this tech – especially when it comes combining robotic and human workforces.
At KDnuggets, we try to keep our finger on the pulse of main events and developments in industry, academia, and technology. We also do our best to look forward to key trends on the horizon. To close out 2017, we recently asked some of the leading experts in Big Data, Data Science, Artificial Intelligence, and Machine Learning for their opinion on the most important developments of 2017 and key trends they 2018. This post, the first in this series of such year-end wrap-ups, considers what happened in Big Data this year, and what may be on the horizon for 2018. "What were the main Big Data related developments in 2017, and what key trends do you see in 2018?"
Decision Tree Model building is one of the most applied technique in analytics vertical. The decision tree model is quick to develop and easy to understand. The technique is simple to learn. A number of business scenarios in lending business / telecom / automobile etc. require decision tree model building. How long the course should take?