How does google understand how to translate '今日はどうですか？' to'How are you doing today?' or vice versa? How do we get to predict a disease spread such as COVID-19 way into the future beforehand? How do automatic Text generation or Text Summarization mechanisms work? The answer is Recurrent Neural Networks. RNNs have been the solution to deal with most problems in Natural language Processing and not only NLP but in Bio-informatics, Financial Forecasting, Sequence modelling etc.
UNDP has launched a new digital strategy to enhance its support to governments in shifting to this transforming environment including by building digital capacity within the organization. The AI strategy of UNDP seeks to increase the understanding of digital technologies and how they can be used to achieve the Sustainable Development Goals. As well as risks and trade-offs that come with them. On the other hand, it is also working to manage the unique ethical issues that can rise from deploying AI in an international development context. The AI readiness tool for stakeholders is building on UNDP's Digital Readiness Assessment piloted.
There have been Kondratiev waves throughout history, commonly referred to as innovation waves, including the invention of electricity, the printing press, and the steam engine. All of these technologies spurred a paradigm shift which resulted in transforming the way the world operated. Today, many believe AI is the next Kondratiev wave and that it will be responsible for transforming how businesses create value, how people work, and ultimately how people live. For businesses to survive the era of AI, they must prepare to abandon legacy technology and invest in new ways of doing things, sometimes reasonably quickly in order to stay relevant. This phenomenon is called the "burning platform" effect, based on the idea that in order to stay competitive, businesses must adopt a radical change strategy as if their current way of doing things was on fire.
Highlights: GANs and classical Deep Learning methods (classification, object detection) are similar, but they are also fundamentally different in nature. Reviewing their properties will be the topic of this post. Therefore, before we proceed further with the GANs series, it will be useful to refresh and recap what is supervised and unsupervised learning. In addition, we will explain the difference between discriminative and generative models. Finally, we will introduce latent variables, since they are an important concept in GANs.
Text freely in natural language with your AI assistant Hopper: let her fly the ship, get her help during combat or ask for a market analysis to maximise your trading profit. She knows a lot - including terrible space jokes. A large 2D open-world universe containing numerous solar systems, space stations and ships to discover. Large means astronomical and impossible-to-fly-through. Be grateful if the jump engine works properly. Find your kidnapped brother while making interstellar friends and enemies.
"A model is as good as the underlying data," said Jayachandran Ramachandran, SVP of Artificial Intelligence Labs at Course5 Intelligence during his MLDS talk "Will evolving regulations stymie AI innovations? He discussed how industries and governments recognise this problem and develop regulations and recommendations. He also touched on the recommendations and implications crelated to European Union's AI regulations draft. Today, most countries have an AI policy and strategies in place. The EU is at the forefront of AI regulations and drafts. "The EU draft in 2021 is acting as a benchmark for other countries," Ramachandran noted. The draft seeks to ensure the AI policy is human-centric, sustainable, secure, inclusive and trustworthy. Additionally, the draft focuses on a seamless transition of AI from the lab to the market. Any system deployed for the users based in the EU will be under the scope of this AI regulation. If the consumers are based outside the EU, they will not be held ...
Over the past few weeks, a bunch of my friends started playing a game called Wordle. Soon, most of my chat rooms were filled with people sharing their Wordle results. I thought it would be a fun challenge to see what strategies an AI could use to solve the puzzle. This post is a deep dive into some of the strategies used to build an AI solver. At a high level, the game's objective is to guess a hidden five-letter word within six tries.
There was a time not so long ago when certain software companies thought the world revolved around them. Those days are gone--as are some of those companies. Today, our world revolves around our customers. We're constantly iterating and innovating in a quest to supply them with the tools, deployment, account reps, developers, and intelligence to succeed. The path to workflow hyperautomation is on the horizon.
Robot umpires have been given a promotion and will be just one step from the major leagues this season. Major League Baseball is expanding its automated strike zone experiment to Triple-A, the highest level of the minor leagues. MLB's website posted a hiring notice seeking seasonal employees to operate the Automated Ball and Strike system. MLB said it is recruiting employees to operate the system for the Albuquerque Isotopes, Charlotte Knights, El Paso Chihuahuas, Las Vegas Aviators, Oklahoma City Dodgers, Reno Aces, Round Rock Express, Sacramento River Cats, Salt Lake Bees, Sugar Land Skeeters and Tacoma Rainiers. The independent Atlantic League became the first American professional league to let a computer call balls and strikes at its All-Star Game in July 2019 and experimented with ABS during the second half of that season. It also was used in the Arizona Fall League for top prospects in 2019, drawing complaints of its calls on breaking balls.
In the near future, it's predicted that these technologies will have an even larger impact on society through activities such as driving fully autonomous vehicles, enabling complex scientific research and facilitating medical discoveries. And cloud computing data centers used by AI and machine learning applications worldwide are already devouring more electrical power per year than some small countries. A research team led by the University of Washington has developed new optical computing hardware for AI and machine learning that is faster and much more energy efficient than conventional electronics. Optical computing noise essentially comes from stray light particles, or photons, that originate from the operation of lasers within the device and background thermal radiation. Of course the optical computer didn't have a human hand for writing, so its form of "handwriting" was to generate digital images that had a style similar to the samples it had studied, but were not identical to them.