Marktechpost.com is a California-based Artificial Intelligence platform for the latest updates in machine learning, deep learning, and data science …

Headlines sounding the alarms that artificial intelligence (AI) will lead humanity to a dystopian future seem to be everywhere. Prominent thought leaders, from Silicon Valley figures to legendary scientists, have warned that should AI evolve into artificial general intelligence (AGI)--AI that is as capable of learning intellectual tasks as humans are--civilization will be under serious threat. Few seeing these warnings, stories, and images could be blamed for believing that the arrival of AGI is imminent. Little surprise, then, that so many media stories and business presentations about machine learning are accompanied by unsettling illustrations featuring humanoid robots. Many of the most respected researchers and academics see things differently, however. They argue that we are decades away from realizing AGI, and some even predict that we won't see AGI in this century. With so much uncertainty, why should executives care about AGI today? The answer is that, while the timing of AGI is uncertain, the disruptive effects it could have on society cannot be understated. Much has already been written about the likely impact of AI and the importance of carefully managing the transition to a more automated world.

Most major companies, including Google, Amazon, Microsoft, Uber, and Tesla, have had their artificial intelligence (AI) and machine learning (ML) systems tricked, evaded, or unintentially misled. Yet despite these high profile failures, most organizations' leaders are largely unaware of their own risk when creating and using AI and ML technologies. This is not entirely the fault of the businesses. An emerging solution is AI/ML-specific insurance. But who will need it and exactly what it will cover are still open questions.

Employing big data and AI prediction models could accelerate the de-confinement process by assessing each person on a risk scale. The coronavirus pandemic has been dealt in the same way by almost every country, self-isolation measures (usually implemented a few weeks too late), economic shutdown, with an aim to flatten the curve in a few months. Now, we are observing a few different ideas on how to transition back to normality. In the U.S., President Trump and several states want to re-open, stressing the economic impact of maintaining quarantine over several months. Other countries are planning a slow re-open, to avoid an even larger pandemic in the winter.

In this seminar we do research in Computational Skepticism, that is, building systems to answer the question "Why Should I Trust an Algorithms Predictions?" As a group, students and any collaborators will be writing a book called "Computational Skepticism." Small groups of students will collaborate on writing a chapter. Two students have already started on their chapter on model interpretability, so you can see what the beginnings of this process looks like here https://maheshwarappa-a.gitbook.io/ads/ Once completed the Computational Skepticism book will be available for free online and published with an ISBN through the Banataba project through a publishing site such as https://www.Blurb.com.

While industrial robots may get more of the attention, the real acceleration in workplace automation will come via software. Why it matters: Robotic process automation (RPA) allows companies to program computer software to emulate the actions of a human worker online. That potentially opens up a much larger portion of the economy to automation at a moment when the pandemic has already forced businesses to go remote. A recent survey from Bain and Company of nearly 800 executives worldwide estimated that the number of companies scaling up such automation technologies is set to double over the next two years -- and that coronavirus will almost certainly accelerate that timeline. Details: Physical robots are certainly getting a boost as companies respond both to the pandemic and the economic downturn, as I wrote in Future last week.

About Malong Technologies Malong Technologies is a global leader in artificial intelligence for product recognition. Since its founding in 2014, the company has focused on advanced deep learning research and development in product recognition for retail applications, with numerous scientific achievements along the way. Malong invented the CurriculumNet weakly-supervised learning algorithm, which won the inaugural WebVision Challenge of CVPR by a large margin. Malong Technologies is on a mission to help the retail industry transform with AI to significantly improve operational efficiency, security and customer experience. Its deep learning breakthroughs are in use by its customers via Malong's RetailAI suite – RetailAI Protect, RetailAI Fresh and RetailAI Cabinet.

ML is no longer just an aspirational technology exclusive to academic and research institutions; it has evolved into a mainstream technology that has the potential to benefit organizations of all sizes. However, a lack of transparency in the ML process and the black box nature of resulting models is a hindrance for improved ML adoption in industries such as financial services and healthcare. For a team developing ML models, the responsibility to explain model predictions increases as the impact of predictions on business outcomes increase. For example, consumers are likely to accept a movie recommendation from an ML model without needing an explanation. The consumer may or may not agree with the recommendation, but the need to justify the prediction is relatively low on the model developers.

A high-end race car engine needs all its components tuned and working together precisely to deliver top-quality performance. The same can be said about the processor inside a quantum computer, whose delicate bits must be adjusted in just the right way before it can perform a calculation. According to a team that includes scientists at JQI and the National Institute of Standards and Technology (NIST), it's an artificial intelligence, that's who. The team's paper in the journal Physical Review Applied outlines a way to teach an AI to make an interconnected set of adjustments to tiny quantum dots, which are among the many promising devices for creating the quantum bits, or "qubits," that would form the switches in a quantum computer's processor. Precisely tweaking the dots is crucial for transforming them into properly functioning qubits, and until now the job had to be done painstakingly by human operators, requiring hours of work to create even a small handful of qubits for a single calculation. A practical quantum computer with many interacting qubits would require far more dots -- and adjustments -- than a human could manage, so the team's accomplishment might bring quantum dot-based processing closer from the realm of theory to engineered reality.

The Artificial Intelligence (AI) As a Service market research report is now available at Market Expertz is an extensive sketch of the business sphere in terms of present and future trends driving the profit matrix. The study also mentions a pointwise outline of the Artificial Intelligence (AI) As a Service market share, market size, industry manufacturers, and regional landscape supported with detailed statistics, diagrams, & charts throwing light on the various noteworthy parameters of the industry landscape. The report includes the latest coverage of the impact of COVID-19 on the Artificial Intelligence (AI) As a Service industry. The incidence has affected nearly every aspect of the business domain. This study evaluates the current scenario and predicts future outcomes of the pandemic on the global economy. The report provides reliable data regarding key investment pockets in the Artificial Intelligence (AI) As a Service market, along with the growth pattern followed by the industry over the forecast period.