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) …
If you want to learn more about exploratory analysis using Pandas, check out Simplilearn's Data Science with Python video, which can help. We can see that columns like LoanAmount and ApplicantIncome contain some extreme values. We need to process this data using data wrangling techniques to normalize and standardize the data. We will now take a look at data wrangling using Pandas as a part of our learning of Data Science with Python. Data wrangling refers to the process of cleaning and unifying messy and complicated data sets.
If you're a data scientist who has been wanting to break into the deep learning realm, here is a great learning resource that can guide you through this journey. It's pretty much an all-inclusive resource that includes all the popular methodologies upon which deep learning depends: CNNs, RNNs, RL, GANs, and much more. The glue that makes it all work is represented by the two most popular frameworks for deep learning pratcitioners, TensorFlow and Keras. This book was a real team effort by a group of consummate professionals: Antonio Gulli (Engineering Director for the Office of the CTO at Google Cloud), Amita Kapoor (Associate Professor in the Department of Electronics at the University of Delhi), and Sujit Pal (Technology Research Director at Elsevier Labs). The resulting text, Deep Learning with TensorFlow 2 and Keras, Second Edition, is an obvious example of what happens when you enlist talented people to write a quality learning resource. I've already recommended this book to my newbie data science students, as I enjoy providing them with good tips for ensuring their success in the field.
We are excited to share that we've just released our 2020 State of AI and Machine Learning. This report illustrates the current state of artificial intelligence and machine learning, showcasing where the industry is as a whole in 2020 compared to 2019. The 2020 report is the output of a cross-industry, large-organization study of senior business leaders and technologists. It details where organizations are within the AI journey and provides a comprehensive look at how they are implementing AI within their business -- from the types of data they leverage to the tools they use and budgets they have. For those who of you who might be in the middle of your own AI projects, this report will help you understand the broader context of your work, what your peers are experiencing, and what dials to turn for AI success.
Kite, which suggests code snippets for developers in real time, today debuted integration with JupyterLab and support for teams using JupyterHub. Data scientists can now get code completions powered by Kite's deep learning, which is trained on over 25 million open-source Python files, as they type in Jupyter notebooks. Using AI to help developers is not an original idea. Nowadays you have startups like DeepCode offering AI-powered code reviews and tech giants like Microsoft working on applying AI to the entire application developer cycle. But Kite stands out with 250,000 monthly developers using its AI-powered developer environment. Kite has been paving the way since its private debut in April 2016, before launching its developer sidekick powered by the cloud publicly in March 2017.
The Council of Europe is working on a future legal framework to regulate the use of artificial intelligence (AI) across all 47 member states. The Council's Ad hoc Committee on Artificial Intelligence (CAHAI) held a three-day meeting on 6-8 July attended by around 150 international experts. The purpose of the meeting was to draw up "concrete proposals on the feasibility study of a future legal framework on artificial intelligence based on human rights, democracy and the rule of law," according to the Council. Representatives from all 47 member states, including Russia, attended the online meeting alongside delegates from'observer states' (USA, Canada, Japan, Mexico, the Vatican and Israel) and AI experts drawn from civil society, academia, and business. Other international organisations such as the EU, OECD and the UN will also contribute to CAHAI's work on potential AI regulation.
For years, voice technology has played a central part in all of our daily lives. We've relied on smart speakers to help us out with our daily routines, used voice notes to chat with family and friends - and even enjoyed the new voice-activated features our favourite social platforms have rolled out in 2020. But in response to this boom in consumers' demand for voice tech, new voice-first tools and platforms are creating more opportunities for conversation between customers and businesses than ever. Today, we'll be diving into how voice technology is revolutionising business in three key areas; customer support, marketing and online accessibility. We'll also be exploring the major and transformative benefits of getting on board with these trends.
What are the best robotics and artificial intelligence stocks to buy today? In this time of uncertainty characterized by volatile market movements, economic contraction, and spiraling unemployment, finding stocks to put your money into seems like an arduous task. Some investors might think that the stock market is acting irrationally and puzzled by the quick recovery of stock prices sin the end of March. Economic reality is that long-term real interest rates are negative, the Federal Reserve is flooding the market with cheap credit, and the current economic slowdown is temporary. This is the perfect environment to buy technology stocks which aren't negatively affected by the coronavirus induced lockdowns and economic slowdown.
Espressive, a pioneer in artificial intelligence (AI) for enterprise service management (ESM) and a 2019 Gartner Cool Vendor, announced the latest advances in its virtual support agent (VSA) Espressive Barista, including Barista Conversational Surveys, the first AI-based survey to inform decision making while triggering actions in real time. Additionally, the new Barista Smart Ticketing capability employs machine learning (ML) to build a predictive model to accurately classify, assign, and prioritize tickets, enabling fast deployment and improved mean time to resolve (MTTR). "In today's economic climate, it has become extremely important for enterprises to implement digital technologies enabling them to innovate, execute, and pivot faster than the competition," said Pat Calhoun, CEO and founder of Espressive. "The challenge CIOs face is that any new technology has to deliver an extremely fast time to value with a positive impact on workforce productivity. Today's announcements further position Espressive customers to meet both of those objectives."
BizClik Media announced the launch of the August edition of Mining Global Magazine. In this issue, Mining Global steps inside Oren, designed by Shell and IBM as the world's first B2B marketplace platform for the mining industry. We continue the theme of data with CRU Group's Director of Technology & Analytics Will Blake, who tells us about the importance of data for mining enterprises. We also speak with Thorsten Scholz, CTO of Forwood Safety, about how his company is harnessing new technologies to transform the mining industry's safety culture, and eradicate workplace fatalities. Elsewhere in the magazine, Ludovic Donati, CDO of Eramet, introduces the innovative technologies that his company has introduced – including artificial intelligence and machine learning – to keep Eramet ahead as the digital era transforms the mining industry.
Target-based high-throughput compound screening dominates conventional one-drug-one-gene drug discovery process. However, the readout from the chemical modulation of a single protein is poorly correlated with phenotypic response of organism, leading to high failure rate in drug development. Chemical-induced gene expression profile provides an attractive solution to phenotype-based screening. However, the use of such data is currently limited by their sparseness, unreliability, and relatively low throughput. Several methods have been proposed to impute missing values for gene expression datasets.