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) …
PyRobot is a light weight, high-level interface which provides hardware independent APIs for robotic manipulation and navigation. This repository also contains the low-level stack for LoCoBot, a low cost mobile manipulator hardware platform. Run the script to install everything (ROS, realsense driver, etc.). If you want to use real LoCoBot robot, please run the following command: Please connect the nuc machine to a realsense camera before running the following commands. Note: To install Python 3 compatible PyRobot, modify -p 2 to -p 3 in the above commands.
To make sure that your experiment logs are reliably stored, Azure Databricks recommends writing logs to DBFS (that is, a log directory under /dbfs/) rather than on the ephemeral cluster file system. For each experiment, start TensorBoard in a unique directory. For each run of your machine learning code in the experiment that generates logs, set the TensorBoard callback or filewriter to write to a subdirectory of the experiment directory. That way, the data in the TensorBoard UI will be separated into runs.
On 23 September 2020, the Committee of Ministers approved the progress report of the Ad hoc Committee on Artificial Intelligence (CAHAI), which sets out the work undertaken and progress towards the fulfilment of the committee's mandate since it was established on 11 September 2019. The progress report sets out a clear roadmap for action towards a Council of Europe legal instrument based on human rights, the rule of law and democracy. Its clear relevance has also been confirmed and reinforced by the recent COVID-19 pandemic. The preliminary feasibility study, providing indications on the legal framework on the design, development of artificial intelligence based on Council of Europe's standards is expected to be examined by the CAHAI at its forthcoming third plenary meeting in December 2020.
If you are a student or a professional looking for various open-source computer vision projects, then, this article is here to help you. The computer vision projects listed below are categorized in an experience-wise manner. All of these projects can be implemented using Python. Face and Eyes Detection is a project that takes in a video image frame as an input and outputs the location of the eyes and face (in x-y coordinates) in that image frame. The script is fairly easy to understand and uses Haar Cascades for detecting the face and the eyes if found in the image frame.
Technology was at first only for specialists. As it evolved, it is now used universally by anyone. Scientists on the other hand started as generalists and ended up as specialists. These opposite directions is because human and artificial intelligence are dealing differently with complexity. In this blog we explore how AI systems already are good as specialist experts, but may become generalists as well.
Mean Squared Error is one of the most used and most straightforward regression-based loss function in Machine Learning and Data Science. It's is used in a range of tasks such as Linear Regression on tabular data to specific use-cases in computer vision, NLP, Reinforcement Learning, etc. In addition to MSE, MAE is also widely used and is highly similar to MSE Loss. Despite being highly used in Machine Learning, it has its share of flaws, which I would like to highlight in this article. There are specific ways to minimize its weaknesses to get better results, which are discussed at the end. The discussion and use-cases are kept relevant to computer vision for simplicity and better understanding.
In recent times there has been a lot of interest in embedding deep learning models into hardware. Energy is of paramount importance when it comes to deep learning model deployment especially at the edge. There is a great blog post on why energy matters for [email protected] by Pete Warden on "Why the future of Machine Learning is Tiny". Energy optimizations for programs (or models) can only be done with a good understanding of the underlying computations. Over the last few years of working with deep learning folks -- hardware architects, micro-kernel coders, model developers, platform programmers, and interviewees (especially interviewees) I have discovered that people understand LSTMs from a qualitative perspective but not well from a quantitative position.
Imperial academics have plotted the roles that AI could play in our future society in a new map that connects reality to science fiction. The Automated Futures Map is designed to show how different AI tools and techniques link together and how they could pave the way to future technologies that have yet to be realised. Existing brain-computer interface technologies could one day prove to be a stepping-stone towards shared dreaming, the recording of our internal monologues, or cyborg rights, according to the map. Similarly, natural language processing technology could advance to allow us to develop earbuds that can translate speech from one language to another instantaneously, or even allow us to communicate with other species. The map was designed by Imperial's Tech Foresight team, who collaborate with Imperial academics to explore breakthrough technologies and assess their potential impact on humans, society and businesses in the future.
Hostile and hateful remarks are thick on the ground on social networks in spite of persistent efforts by Facebook, Twitter, Reddit and YouTube to tone them down. Now researchers at the OpenWeb platform have turned to artificial intelligence to moderate internet users' comments before they are even posted. The study conducted by OpenWeb and Perspective API analyzed 400,000 comments that some 50,000 users were preparing to post on sites like AOL, Salon, Newsweek, RT and Sky Sports. Some of these users received a feedback message or nudge from a machine learning algorithm to the effect that the text they were preparing to post might be insulting, or against the rules for the forum they were using. Instead of rejecting comments it found to be suspect, the moderation algorithm then invited their authors to reformulate what they had written.