Goto

Collaborating Authors

Deep Learning


Vectorization in Deep Learning

#artificialintelligence

In the context of high-level languages like Python, Matlab, and R, the term vectorization describes the use of optimized, pre-compiled code written in a low-level language (e.g. C) to perform mathematical operations over a sequence of data. This is done in place of an explicit iteration written in the native language code (e.g. a "for-loop" written in Python). Vectorization allows the elimination of the for-loops in python code. It is especially important in Deep learning as we are dealing with large numbers of datasets.


Top 10 most popular AI trends of the 2022 year

#artificialintelligence

The tech media outlet Toolbox featured the views of 10 experts on "How will AI evolve in the next year?" Edge technology that experts should pay attention to next year was also intensively discussed. The first place was occupied by MIT's Neil Thompson research team featuring an article on the cost of energy to train deep learning systems. As a result of analyzing the improvements of the image classifier, the research team found that "to cut the error rate in half, it can be expected that 500 times more computational resources are required." "The rising cost requires researchers to devise more efficient ways to solve these problems, otherwise we will give up research on these problems, and progress will be difficult," he said.



Yann LeCun's vision for creating autonomous machines

#artificialintelligence

We are excited to bring Transform 2022 back in-person July 19 and virtually July 20 - 28. Join AI and data leaders for insightful talks and exciting networking opportunities. In the midst of the heated debate about AI sentience, conscious machines and artificial general intelligence, Yann LeCun, Chief AI Scientist at Meta, published a blueprint for creating "autonomous machine intelligence." LeCun has compiled his ideas in a paper that draws inspiration from progress in machine learning, robotics, neuroscience and cognitive science. He lays out a roadmap for creating AI that can model and understand the world, reason and plan to do tasks on different timescales. While the paper is not a scholarly document, it provides a very interesting framework for thinking about the different pieces needed to replicate animal and human intelligence. It also shows how the mindset of LeCun, an award-winning pioneer of deep learning, has changed and why he thinks current approaches to AI will not get us to human-level AI.


Best 15 real-life examples of machine learning - Dataconomy

#artificialintelligence

Numerous examples of machine learning show that machine learning (ML) can be extremely useful in a variety of crucial applications, including data mining, natural language processing, picture recognition, and expert systems. In all of these areas and more, ML offers viable solutions, and it is destined to be a cornerstone of our post-apocalyptic civilization. The history of machine learning shows that a good grasp of the machine learning lifecycle increase machine learning benefits for businesses significantly. There are many uncommon machine learning examples that prove this, and you will find the best ones in this article. Machine learning uses statistical methods to increase a computer's intelligence, assisting in the automatic utilization of all business data. Due to growing reliance on machine learning technologies, humans' lifestyles have undergone a significant transformation. We use Google Assistant, which uses ML principles, as an example.


8 Ways You Can 'Level Up' Your Machine Learning Projects

#artificialintelligence

Need to classify data or predict outcomes? Are you struggling with your machine learning (Machine Learning) project? There are various techniques that can improve the situation. Some of the eight methods discussed below will dramatically accelerate the Machine Learning process, and others will not only accelerate the process, but will also help you build better models. Not all of these techniques will be suitable for a particular project.


iiot ai_2022-07-01_03-34-52.xlsx

#artificialintelligence

The graph represents a network of 1,579 Twitter users whose tweets in the requested range contained "iiot ai", or who were replied to or mentioned in those tweets. The network was obtained from the NodeXL Graph Server on Friday, 01 July 2022 at 10:42 UTC. The requested start date was Friday, 01 July 2022 at 00:01 UTC and the maximum number of tweets (going backward in time) was 7,500. The tweets in the network were tweeted over the 2-day, 4-hour, 0-minute period from Tuesday, 28 June 2022 at 20:00 UTC to Friday, 01 July 2022 at 00:01 UTC. Additional tweets that were mentioned in this data set were also collected from prior time periods.


Understanding Human-Machine Collaboration(Artificial Intelligence)

#artificialintelligence

Abstract: We present a model of sense-making that greatly facilitates the collaboration between an intelligent analyst and a knowledge-based agent. It is a general model grounded in the science of evidence and the scientific method of hypothesis generation and testing, where sense-making hypotheses that explain an observation are generated, relevant evidence is then discovered, and the hypotheses are tested based on the discovered evidence. We illustrate how the model enables an analyst to directly instruct the agent to understand situations involving the possible production of weapons (e.g., chemical warfare agents) and how the agent becomes increasingly more competent in understanding other situations from that domain (e.g., possible production of centrifuge-enriched uranium or of stealth fighter aircraft) Abstract: There is a growing desire to create computer systems that can communicate effectively to collaborate with humans on complex, open-ended activities. Assessing these systems presents significant challenges. We describe a framework for evaluating systems engaged in open-ended complex scenarios where evaluators do not have the luxury of comparing performance to a single right answer.


From patterns to deep learning

#artificialintelligence

From styles to deep getting to know Computers prepare facts via way of means of the use of algorithms. These are math formulations or commands that observe a step-via way of means of step method. For example, the stairs in a single set of rules may train a pc to organization snapshots with comparable styles. In a few cases, which includes the cat pictures, humans assist computer systems in kind out incorrect information. In different cases, the algorithms may assist the pc to perceive errors and examine them.


Summer 2022 - Researcher positions in artificial intelligence and machine learning -- FCAI

#artificialintelligence

We develop reinforcement learning techniques to enable interaction across multiple agents including AIs and humans, with potential applications from AI-assisted design to autonomous driving. Methodological contexts of the research include deep reinforcement learning, inverse reinforcement learning, hierarchical reinforcement learning as well as multi-agent and multi-objective reinforcement learning. FCAI is working on a new paradigm of AI-assisted design that aims to cooperate with designers by supporting and leveraging the creativity and problem-solving of designers. The challenge for such AI is how to infer designers' goals and then help them without being needlessly disruptive. We use generative user models to reason about designers' goals, reasoning, and capabilities. In this call, FCAI is looking for a postdoctoral scholar or research fellow to join our effort to develop AI-assisted design. Suitable backgrounds include deep reinforcement learning, Bayesian inference, cooperative AI, computational cognitive modelling, and user modelling. Computational rationality is an emerging integrative theory of intelligence in humans and machines (1) with applications in human-computer interaction, cooperative AI, and robotics. The theory assumes that observable human behavior is generated by cognitive mechanisms that are adapted to the structure of not only the environment but also the mind and brain itself (2).