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) …
Many times we listen to speak about machine learning, but it is important to know that there are other pipelines before machine learning which play a significant role in the study of Big Data. Some examples are ETL (extract, transform and load) or NLP(natural language processing). Nowadays, in particular, NLP pipeline is taking more and more space in Data Science. So, what is Natural Language Processing? Natural Language Processing is a process that permits Data Scientist or Data Analyst to extract important information from human language. For example, with NLP it is possible to find an important pattern by studying texts in posts or comments available on a social network.
Wind farms have traditionally made less money for the electricity they produce because they have been unable to predict how windy it will be tomorrow. "The way a lot of power markets work is you have to schedule your assets a day ahead," said Michael Terrell, the head of energy market strategy at Google. "And you tend to get compensated higher when you do that than if you sell into the market real-time. "Well, how do variable assets like wind schedule a day ahead when you don't know the wind is going to blow?" Terrell asked, "and how can you actually reserve your place in line?" Here's how: Google and the Google-owned Artificial Intelligence firm DeepMind combined weather data with power data from 700 megawatts of wind energy that Google sources in the Central United States. Using machine learning, they have been able to better predict wind production, better predict electricity supply and demand, and as a result, reduce operating costs. "What we've been doing is working in partnership with the DeepMind team to use machine learning to take the weather data that's available publicly, actually forecast what we think the wind production will be the next day, and bid that wind into the day-ahead markets," Terrell said in a recent seminar hosted by the Stanford Precourt Institute of Energy. Stanford University posted video of the seminar last week. The result has been a 20 percent increase in revenue for wind farms, Terrell said. The Department of Energy listed improved wind forecasting as a first priority in its 2015 Wind Vision report, largely to improve reliability: "Improve Wind Resource Characterization," the report said at the top of its list of goals. "Collect data and develop models to improve wind forecasting at multiple temporal scales--e.g., minutes, hours, days, months, years." Google's goal has been more sweeping: to scrub carbon entirely from its energy portfolio, which consumes as much power as two San Franciscos. Google achieved an initial milestone by matching its annual energy use with its annual renewable-energy procurement, Terrell said. But the company has not been carbon-free in every location at every hour, which is now its new goal--what Terrell calls its "24x7 carbon-free" goal. "We're really starting to turn our efforts in this direction, and we're finding that it's not something that's easy to do.
Data scientists come from different backgrounds. In today's agile environment, it is highly essential to respond quickly to customer needs and deliver value. Faster value provides more wins for the customer and hence more wins for the organization. Information Technology is always under immense pressure to increase agility and speed up delivery of new functionality to the business. A particular point of pressure is the deployment of new or enhanced application code at the frequency and immediacy demanded by typical digital transformation.
One of the foundations of the bio revolution now underway is the knowledge base was built over 13 years as scientists mapped the human genome. However, the power of that map to fuel innovation only materialized when it became cheaper and quicker to sequence DNA because of advances in computing. Today, the cost of DNA sequencing is decreasing at a rate faster than Moore's Law. In 2003, mapping the genome cost about $3 billion; by 2016, that had dropped to less than $1,000 and could be less than $100 in less than a decade. Scientists sequenced the coronavirus responsible for COVID-19 in weeks rather than the months it took to sequence the virus responsible for the original SARS epidemic.
Machine learning, the subset of artificial intelligence that teaches computers to perform tasks through examples and experience, is a hot area of research and development. Many of the applications we use daily use machine learning algorithms, including AI assistants, web search and machine translation. Your social media news feed is powered by a machine learning algorithm. The recommended videos you see on YouTube and Netflix are the result of a machine learning model. And Spotify's Discover Weekly draws on the power of machine learning algorithms to create a list of songs that conform to your preferences. But machine learning comes in many different flavors.
I am regularly asked to summarize my many posts. I thought it would be a good idea to publish on this blog, every Monday, some of the most relevant articles that I have already shared with you on my social networks. Today I will share some of the most relevant articles about Artificial Intelligence and in what form you can find it in today's life. I will also comment on the articles. After the COVID-19 pandemic is over and the economy reopens, many students will resume work on their careers.
Generative Adversarial Networks (GANs) are a trend nowadays in various unsupervised learning applications. They are applied in animation and gaming with a full swing due to their capability to produce new images when trained on a set of similar but different images. This model is basically a deep generative model composed of two networks – a generator and a discriminator. The Deep Convolutional Neural Network is one of the variants of GAN where convolutional layers are added to the generator and discriminator networks. In this article, we will train the Deep Convolutional Generative Adversarial Network on Fashion MNIST training images in order to generate a new set of fashion apparel images.
The world of artificial intelligence has exploded in recent years. Computers armed with AI do everything from drive cars to pick movies you'll probably like. Some have warned we're putting too much trust in computers that appear to do wondrous things. But what exactly do people mean when they talk about artificial intelligence? It's hard to find a universally accepted definition of artificial intelligence.
As the world grows increasingly connected, growing concern regarding the influence of artificial intelligence (AI) has been bubbling to the surface, affecting perceptions by industries big and small along with the general populace. Spurred on by sensationalized media predictions of AI taking over human decision-making and silver-screen tales of robot revolutions, there is a fear of allowing AI or its cousin, the Internet of Things (IoT), into our lives. Here is AI's man behind the curtain. One of the biggest sticking points is the popular – yet mistaken – notion that AI will cost people their jobs. In truth, the situation is just the opposite.
As the Chinese Professional Baseball League starts its season, one team has gotten creative about "filling the stands" during the coronavirus pandemic. The Rakuten Monkeys will play games in front of robot mannequins in the audience dressed up as fans, according to the CPBL official website. "You call that a fastball? I haven't seen anything that slow since Pentium II!" You swing like your tension-amplification mechanism was sent to the wrong 3-D printer!" "C'mon, let's score some runs!