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Artificial intelligence for the masses

#artificialintelligence

It takes real intelligence and plenty of collaborative muscle to harness the potential of artificial intelligence. Most of us can barely grasp the concept of human-made machines learning how to process and analyze enormous amounts of data, then using that mass of information to understand things at new scales and in new combinations, delivering useful insights that our brains would never be able to produce on their own. Now University of Delaware Prof. Rudolf Eigenmann, interim chair of the Department of Computer and Information Sciences and professor of electrical and computer engineering, is playing a critical role in a new $20 million National Science Foundation-supported project designed to expand access to artificial intelligence. AI for the masses, you might call it. The project, called the NSF AI Institute for Intelligent Cyberinfrastructure with Computational Learning in the Environment (ICICLE), is one of 11 new National Artificial Intelligence Research Institutes the NSF announced recently. It is the second year of such investment by NSF.


The U.N. Warns That AI Can Pose A Threat To Human Rights

NPR Technology

The United Nations High Commissioner for Human Rights Michelle Bachelet speaks at a climate event in Madrid in 2019. A recent report of hers warns of the threats that AI can pose to human rights. The United Nations High Commissioner for Human Rights Michelle Bachelet speaks at a climate event in Madrid in 2019. A recent report of hers warns of the threats that AI can pose to human rights. The United Nations' human rights chief has called on member states to put a moratorium on the sale and use of artificial intelligence systems until the "negative, even catastrophic" risks they pose can be addressed. The remarks by U.N. High Commissioner for Human Rights Michelle Bachelet were in reference to a new report on the subject released in Geneva.


AI hardware pioneer Cerebras expands access in partnership with cloud vendor Cirrascale

ZDNet

The battle for artificial intelligence hardware keeps moving through phases. Three years ago, chip startups such as Habana Labs, Graphcore, and Cerebras Systems grabbed the spotlight with special semiconductors designed expressly for deep learning. Those vendors then moved on to selling whole systems, with newcomers such as SambaNova Systems starting out with that premise. Now, the action is proceeding to a new phase, where vendors are partnering with cloud operators to challenge the entrenched place of Nvidia as the vendor of choice in cloud AI. Cerebras on Thursday announced a partnership with cloud operator Cirrascale to allow users to rent capacity on Cerebras's CS-2 AI machine running in Cirrascale cloud data centers.


Join Us for RE:WIRED--Conversations on Humanity's Biggest Bets

WIRED

Everything, it seems, is on the line right now. Is mRNA technology on the verge of pulling us out of the pandemic, or will a wily, evolving virus bring us several more years of testing, booster shots, uncertainty, and angst? Will AI liberate us with amazing new medicines, materials, and modes of entertainment, or subtly enslave us in an algorithmically guided world where none of our choices is truly our own? Will we stop the deadly climb of global temperatures or just hunker down behind higher flood defenses and more powerful air-conditioners? Will ransomware be tamed, or will it bring down governments?


Naive Bayes Algorithm: A Complete guide for Data Science Enthusiasts

#artificialintelligence

In this article, we will discuss the mathematical intuition behind Naive Bayes Classifiers, and we'll also see how to implement this on Python. This model is easy to build and is mostly used for large datasets. It is a probabilistic machine learning model that is used for classification problems. The core of the classifier depends on the Bayes theorem with an assumption of independence among predictors. That means changing the value of a feature doesn't change the value of another feature.


Redefining Cancer Treatment- The Memorial Sloan Way

#artificialintelligence

Whenever a patient has symptoms of cancer, the cancer tumour is taken out and sequenced. Genetic information in the tumor cell is stored in the form of DNA. It is then transcribed to form RNA which is then translated to form proteins/amino acids. In case of a mutation, or a mistake in DNA sequence, the resultant amino acid is affected giving rise to a variation for the particular gene. Thousands of genetic mutations may be present in the sequence. We need to distinguish the malignant mutations (drivers leading to tumour growth) from the benign (passenger) ones.


Machine Learning with No-Code

#artificialintelligence

AI is a subdomain of Machine Learning (ML). The focus of AI or ML requires math and programming. No-Code options for creating an AI based solution have increased and are in the mainstream within several Microsoft products. No-Code tools provide a graphical interface that provide the same quality solution as scripting in Python. Machine learning is a technique that uses mathematics and statistics to create a model that can predict unknown values.


EETimes - AI Startup Deep Vision Raises Funds, Preps Next Chip

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Edge AI chip startup Deep Vision has raised $35 million in a series B round of funding led by Tiger Global, joined by existing investors Exfinity Venture Partners, Silicon Motion and Western Digital. The company began shipping its first-generation chip last year. ARA-1 is designed for power-efficient, low-latency edge AI processing in applications like smart retail, smart city and robotics. While the company's name suggests a focus on convolutional neural networks, ARA-1 can also accelerate natural language processing with support for complex networks such as long short-term memory (LSTMs) and recurrent neural networks (RNNs). A second-generation chip, ARA-2 with additional features for accelerating LSTMs and RNNs will launch next year.


Do We Have A Backup Plan If Artificial Intelligence Causes Unemployment?

#artificialintelligence

Major disruptive technologies such as artificial intelligence have penetrated into the global tech market to enhance productivity for better yield. Organizations and factories are instigated to implement multiple AI models for automating tasks and generating meaningful in-depth insights. But this increase in human-machine interaction has created a concern among human employees, especially those who are elders in the workplace. How will the human employees earn to live a standard life? These types of questions are coming into the minds of people after robots and AI models are automating processes and dealing with loads of data efficiently and effectively. Do we really have a backup plan if seriously artificial intelligence causes unemployment in the next few years?


DCGAN from Scratch with Tensorflow Keras -- Create Fake Images from CELEB-A Dataset

#artificialintelligence

Generator: the generator generates new data instances that are "similar" to the training data, in our case celebA images. Generator takes random latent vector and outputs a "fake" image of the same size as our reshaped celebA image. Discriminator: the discriminator evaluate the authenticity of provided images; it classifies the images from the generator and the original image. Discriminator takes true of fake images and outputs the probability estimate ranging between 0 and 1. Here, D refers to the discriminator network, while G obviously refers to the generator.