Collaborating Authors

Zapata raises $38 million for quantum machine learning


Zapata Computing has raised $38 million for its quantum computing enterprise software platform. The figure, which brings its total funding to over $64 million, will be put toward Zapata's core mission: "Delivering quantum advantage for customers through real business use cases." Quantum computing leverages qubits (unlike bits that can only be in a state of 0 or 1, qubits can also be in a superposition of the two) to perform computations that would be much more difficult, or simply not feasible, for a classical computer. Unlike most quantum computing startups that build the hardware, Zapata is focused on the algorithms and software that sit on top. Based in Boston, Zapata has one product: its hardware-agnostic Orquestra quantum computing platform.

Driverless cars and smart cities: the amazing future summarised in 5 online talks


This article is part of KrASIA's partnership with Web Summit. The last 12 months have seen decisive change in the way we spend our free time. Mobility solutions are becoming increasingly popular, with driverless vehicles popping up across the world, while our urban spaces are evolving into smart city projects. Web Summit's lifestyle content covers it all. What CNN calls "Europe's largest tech event" gathers experts from the industries that play vital roles in our lifestyles.

Normal Distribution and Machine Learning


Normal Distribution is an important concept in statistics and the backbone of Machine Learning. A Data Scientist needs to know about Normal Distribution when they work with Linear Models(perform well if the data is normally distributed), Central Limit Theorem, and exploratory data analysis. As discovered by Carl Friedrich Gauss, Normal Distribution/Gaussian Distribution is a continuous probability distribution. It has a bell-shaped curve that is symmetrical from the mean point to both halves of the curve. A continuous random variable "x" is said to follow a normal distribution with parameter μ(mean) and σ(standard deviation), if it's probability density function is given by, This is also called a normal variate.

Artificial Intelligence and Human Lives: Looking forwards 2025-2070


In the darkest days of a dark year it's good to think about our possible futures together. This talk is about wealth, power, and intelligence, and how we are communicating these due to the digital transformation. Is there a chance for a positive digital future, and if so what would it look like? Joanna Bryson is Professor of Ethics and Technology at the Hertie School of Governance in Berlin, Germany. She holds degrees in psychology and artificial intelligence from the University of Chicago (BA), the University of Edinburgh (MSc and MPhil), and Massachusetts Institute of Technology (PhD).

A Complete Neural Network Algorithm from Scratch in Python


The Neural Network has been developed to mimic a human brain. Though we are not there yet, neural networks are very efficient in machine learning. It was popular in the 1980s and 1990s. Recently it has become more popular. Probably because computers are fast enough to run a large neural network in a reasonable time.

How do you measure trust in deep learning?


This article is part of our reviews of AI research papers, a series of posts that explore the latest findings in artificial intelligence. Whether it's diagnosing patients or driving cars, we want to know whether we can trust a person before assigning them a sensitive task. In the human world, we have different ways to establish and measure trustworthiness. In artificial intelligence, the establishment of trust is still developing. In the past years, deep learning has proven to be remarkably good at difficult tasks in computer vision, natural language processing, and other fields that were previously off-limits for computers.

How Enhanced AI Could Be Achieved Through Crowdsourcing Morality


With the rapid acquaintance of artificial intelligence (AI) the qualms and questions about whether robots could act immorally or soon choose to harm humans have also been raised. Some people are calling to put bans on robotics research while others are calling to conduct more research to be aware of how AI might be controlled. But how can robots learn ethical and moral behavior if there is no "user manual" for being human? This question of robotic ethics is making everyone apprehensive. We are concerned about the lack of understanding and empathy in machines like how so-called'calculating machines' are going to know that what is wrong and how to do the right thing, and even how we are going to judge and penalize by beings of steel and silicon.

Eureka: A family of computer scientists developed a blueprint for machine consciousness

CMU School of Computer Science

Renowned researchers Manuel Blum and Lenore Blum have devoted their entire lives to the study of computer science with a particular focus on consciousness. They've authored dozens of papers and taught for decades at prestigious Carnegie Mellon University. And, just recently, they published new research that could serve as a blueprint for developing and demonstrating machine consciousness. That paper, titled "A Theoretical Computer Science Perspective on Consciousness," may only a be a pre-print paper, but even if it crashes and burns at peer-review (it almost surely won't) it'll still hold an incredible distinction in the world of theoretical computer science. The Blum's are joined by a third collaborator, one Avrim Blum, their son.

Do You Have a Conflict of Interest? This Robotic Assistant May Find It First


What should science do about conflicts of interest? When they are identified, they become an obstacle to objectivity -- a key tenet and a cornerstone of academia and research -- and the truth behind what scientists report is called into question. Sometimes a conflict of interest is clear cut. Researchers who fail to disclose a funding source with a business interest in the outcome are often likely to undermine the legitimacy of their findings. Additionally, when an author of a paper has worked extensively on other research with an editor of a journal, the conflict of interest can look glaringly obvious.

Software developers face mounting pressure in 2021


The former Forrester Research Director Chris Mines predicted in 2019 that the world of software development was set for some big changes in 2020. We had no idea that a year later, almost every development shop would be a remote development shop. It makes the curated list of "remote-friendly" companies on GitHub a nostalgic reminder of a simpler, pre-pandemic time. Most developers adjusted well to the changes in 2020, certainly compared to other professions. Working hours increased and work weeks lengthened, but our digital world didn't come crashing down like other sectors of the global economy.