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. That question has been burning through technology circles for several weeks now, ever since a lone Google engineer claimed his LAMDA model had achieved both self-awareness and a soul. While this is an important question, it is not something that the enterprise needs to concern itself with just yet. Even if such an algorithm were to arise, would it be all that useful in a practical sense? AI sentience has been a topic of debate for decades, but it got a kick-start last month when Google engineer, Blake Lemoine, posted conversations with a chatbot that he claimed proved it was sentient.

HealthCare Global Enterprises Ltd (HCG) on Monday announced that it has deployed Sigtuple's AI100 making HCG the first hospital chain to equip the Hematopathology labs across its network with AI-powered screening solutions for cancer detection and disease management. According to the company's press statement, SigTuple's AI100 is the premier solution for AI-assisted digital hematopathology. It is also the only digital hematopathology solution available that is economical and robust enough for wide-scale adoption, it claimed. "As manual microscopy is still the standard in diagnosing several critical disorders like cancers, infections, etc., in the absence of a pathologist at site in laboratories outside urban areas, these samples need to be shipped to central reference laboratories for review. Apart from the logistic challenges and associated delays in turnaround times, there is also limited expertise available for providing high quality diagnostics at remote locations," it stated.

Experts in the United Kingdom are designing AI-powered robots to work in some of the most hazardous places on Earth and outer space. University of Manchester researchers have been advising government and energy sector leaders on the safe development of AI robots being used in extreme environments. The University of Manchester said "hot robotic" systems were originally designed to work in radioactive environments in decommissioned nuclear reactors, but found a new use in the fields of nuclear fusion power, agriculture, the energy sector, and even space exploration. The university said in a statement: "As part of an ambitious R&D program to maintain UK leadership in robotic technologies, Manchester experts are applying AI technologies to'hot robotics' as they will increasingly need to act independently of human operators as they enter a range of danger zones to carry out highly complex tasks. "An important challenge in the nuclear industry is to improve robot autonomy so that the technology can be used to deliver safer, faster and cheaper decommissioning of legacy power stations and other radioactive facilities at sites such as Sellafield and Dounreay.

To understand how organizations like Google, Amazon, and even Udemy use machine learning and artificial intelligence (AI) to extract meaning and insights from enormous data sets, this machine learning course will provide you with the essentials. According to Glassdoor and Indeed, data scientists earn an average income of $120,000, and that is just the norm! When it comes to being attractive, data scientists are already there. In a highly competitive job market, it is tough to keep them after they have been hired. People with a unique mix of scientific training, computer expertise, and analytical abilities are hard to find.

Inside the enterprise, missing or ineffectively managed information can be extremely costly. Not only can it lead to business inefficiencies, but it can even result in lost business opportunities. Semantics can play a key role in making sure that all enterprise information is readily available, especially the unstructured data. Semantics is the study of the meaning of words and sentences. It uses the relations of linguistic forms to non-linguistic concepts and mental representations to explain how sentences are understood by native speakers.

Find open roles in Artificial Intelligence (AI), Machine Learning (ML), Natural Language Processing (NLP), Computer Vision (CV), Data Engineering, Data Analytics, Big Data, and Data Science in general, filtered by job title or popular skill, toolset and products used.

You'll have access to tons of data, but you'll also be cursed with tons of noise. Most humans are good and amazing, so every bad behavior is hidden behind a swarm of good behavior. To identify the baddies, you will need to look for clues as to what is going on, to look for patterns, to ask intelligent questions and look for answers, to deal with uncertainty, to find the story behind the data, to turn the data into information. You'll need to be a detective. You will create new ML models and techniques and expand on the ones we have in production.

Quantum computing promises to improve our ability to perform some critical computational tasks in the future. Machine learning is changing the way we use computers in our present everyday life and in science. It is natural to seek connections between these two emerging approaches to computing, in the hope of reaping multiple benefits. The search for connecting links has just started, but we are already seeing a lot of potential in this wild, unexplored territory. We present here two new research articles: "Precise measurement of quantum observables with neural-network estimators," published in Physical Review Research, and "Fermionic neural-network states for ab-initio electronic structure," published in Nature Communications.

New Delhi: Centre of Excellence on Satellite & Unmanned Remote Vehicle Initiative (CoE-SURVEI) has developed an Artificial Intelligence-based software which can automatically detect change on ground, including unauthorised constructions and encroachments in a time series using Satellite Imagery. The CoE-SURVEI, established by Directorate General Defence Estates (DGDE) at National Institute of Defence Estates Management, leverages latest technologies in survey viz. The CoE was inaugurated by Raksha Mantri Rajnath Singh on December 16, 2021. This Change Detection Software has been developed by CoE-SURVEI in collaboration with knowledge partner Bhabha Atomic Research Centre (BARC), Visakhapatnam. Presently, the tool uses National Remote Sensing Centre (NRSC) Cartosat-3 imagery with trained software.

Interpretable machine learning techniques are becoming more popular among the data science community as more and more complex machine learning algorithms are adopted which are not easily interpretable. Model-Agnostic Interpretation techniques do not care about the underlying models, but they have the capability to interpret the model and provide insightful model interpretation. Some of the popular model-agnostic interpretation techniques for machine learning models are partial dependence plots (PDP), permutation feature importance (PFI), LIME, and SHAP. These model-agnostic interpretation techniques can lead to wrong insights or conclusions if applied incorrectly. In this article, we will discuss some of the popular 8 pitfalls to avoid while using an interpretation technique.