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) …
February 12, 2020When German internist and surgeon Georg Kelling performed the first laparoscopic surgery in 1901, he likely hadn't envisioned that machines would one day follow in his footsteps. But today, robotic surgery is a health-care reality that promises certain benefits, like improved surgical precision that can contribute to quicker patient healing times. Still, widespread adoption of the technology has remained elusive. "The traditional approach to robotic surgery brings with it a lot of complexity and high cost," says Marcus Heneen, a design director at McKinsey Design. Today's surgical robots, Marcus explains, tend to situate the surgeon at a console in a non-sterile environment away from the patient.
When it comes to our health, especially in matters of life and death, the promise of artificial intelligence (AI) to improve outcomes is very intriguing. While there is still much to overcome to achieve AI-dependent health care, most notably data privacy concerns and fears of mismanaged care due to machine error and lack of human oversight, there is sufficient potential that governments, tech companies, and healthcare providers are willing to invest and test out AI-powered tools and solutions. Here are five of the AI advances in healthcare that appear to have the most potential. With an estimated value of $40 billion to healthcare, robots can analyze data from pre-op medical records to guide a surgeon's instrument during surgery, which can lead to a 21% reduction in a patient's hospital stay. Robot-assisted surgery is considered "minimally invasive" so patients won't need to heal from large incisions.
Robotic motion-planning problems, such as a UAV flying fast in a partially-known environment or a robot arm moving around cluttered objects, require finding collision-free paths quickly. Typically, this is solved by constructing a graph, where vertices represent robot configurations and edges represent potentially valid movements of the robot between theses configurations. The main computational bottlenecks are expensive edge evaluations to check for collisions. State of the art planning methods do not reason about the optimal sequence of edges to evaluate in order to find a collision free path quickly. In this paper, we do so by drawing a novel equivalence between motion planning and the Bayesian active learning paradigm of decision region determination (DRD).
The Evaluation Committee has completed the evaluations for the AI Time Journal TOP 25 Artificial Intelligence Companies 2019. The objective of the AI Time Journal TOP 25 Artificial Intelligence Companies 2019 Initiative is to give recognition and showcase AI companies for their contribution in 2019 to applying Artificial Intelligence, Machine Learning and Deep Learning to solve significant and complex problems and improve people's lives in a multitude of domains including Healthcare, Education, Finance, Autonomous Vehicles and more. Note: companies that employ evaluation committee members have not been included in the evaluations. Alvin Foo: "The adoption of Artificial Intelligence, Deep Learning and Machine Learning to facilitate human decision-making will continue to accelerate. While it creates opportunities to automate, it will also open up new challenges for IT team to address the potential increase in cyberattack. The advancement of AI provides a scalable cybersecurity solution for companies to automate and protect their IT assets. The future of cybersecurity will be AI-powered!"
Artificial intelligence, machine learning, neural networks or whatever other fancy terms industry is coming out with for what is defined as the sophisticated computer technology that is becoming widely utilized to understand and improve business and customer experiences. I assume you have heard of it before, but the way it is defined today is an area of computer science that emphasizes the creation of intelligent machines that work and react like humans. Digital IQ, as the measurement of how well an organization can understand its business processes from a variety of critical perspectives, will play an increasingly important role in every digital transformation strategy as more enterprises come to the realization that they must have visibility into their operations. Digital intelligence solutions will help organizations increase this business-critical ability by optimizing automation initiatives and complementing platforms like RPA and BPM. In 2020, more organizations will adopt digital intelligence technologies into their overarching digital transformation initiatives, as enterprises realize that these solutions illuminate paths to improved customer experience, reduce operating costs and sharpen competitive advantage.
Automation fears distract from the real problem: too few blue-collar workers. This article is part of an MIT SMR initiative exploring how technology is reshaping the practice of management. Following the Great Recession, anxiety intensified over the prospect of automation causing permanent, widespread unemployment. Feeding on public alarm, a large number of studies assessed the likely impact of future automation on jobs. Although some touted the potential for job creation, others predicted catastrophic job loss.
This article may come about 30 years too early but I hope it will be fun for you to read it as it was for me to write it. It assumes, at least, the existence of machine cognition - AI agents or systems able to reason, to understand the world model and concepts, to properly interact with environment changes. The Singularity is not required. They do not have to pass Turing tests nor do they need to have some awesome language skills. At present, in 2019, we have a number of chatbots and assistants largely available to the public.
In a research field off Highway 54 last autumn, corn stalks shimmered in rows 40-feet deep. Girish Chowdhary, an agricultural engineer at the University of Illinois at Urbana-Champaign, bent to place a small white robot at the edge of a row marked 103. The robot, named TerraSentia, resembled a souped up version of a lawn mower, with all-terrain wheels and a high-resolution camera on each side. In much the same way that self-driving cars "see" their surroundings, TerraSentia navigates a field by sending out thousands of laser pulses to scan its environment. A few clicks on a tablet were all that were needed to orient the robot at the start of the row before it took off, squeaking slightly as it drove over ruts in the field.
Data-driven approaches to solving robotic tasks have gained a lot of traction in recent years. However, most existing policies are trained on large-scale datasets collected in curated lab settings. If we aim to deploy these models in unstructured visual environments like people's homes, they will be unable to cope with the mismatch in data distribution. In such light, we present the first systematic effort in collecting a large dataset for robotic grasping in homes. First, to scale and parallelize data collection, we built a low cost mobile manipulator assembled for under 3K USD.
Movement Primitives (MP) are a well-established approach for representing modular and re-usable robot movement generators. Many state-of-the-art robot learning successes are based MPs, due to their compact representation of the inherently continuous and high dimensional robot movements. A major goal in robot learning is to combine multiple MPs as building blocks in a modular control architecture to solve complex tasks. To this effect, a MP representation has to allow for blending between motions, adapting to altered task variables, and co-activating multiple MPs in parallel. We present a probabilistic formulation of the MP concept that maintains a distribution over trajectories.