Morgan: IBM Watson Creates World’s First Movie Trailer Using AI

The 20th Century Fox teamed up with IBM Research to create the world’s first cognitive movie trailer with the help of the AI bot IBM Watson. It chose 10 moments out of the horror flick Morgan and then a human editor stitched them to come up with a trailer.

The IBM Watson is getting better every day. Last time, we heard about it when it detected a rare form of leukemia in a patient’s body and when it was used to drive a bus. Now, doctor IBM Watson is preparing for a Hollywood debut. The supercomputer IBM Watson was used to create the trailer of the 20th Century Fox horror movie Morgan.
For a scary movie, an important thing is how a person digests it. “Our team was faced with the challenge of not only teaching a system to understand, “what is scary”, but then to create a trailer that would be considered “frightening and suspenseful” by a majority of viewers,” writes Michael Zimmerman for IBM.

Over 100 horror movie trailers were fed to the IBM Watson in order to give IBM Watson the “feel” of a horror movie. Though in reality, it is just some binary numbers for the AI bot.
The trailer videos were segmented into small moments on which IBM Watson performed an analysis of audio, video, and how each scene was composed. It helped Watson detect what scenes were depicting, for example, a frightened person or an eerie moment.
After watching the 90-minute Morgan, IBM Watson came up with 10 moments (6-minutes of total length) that “would be the best candidate for the trailer”.
The final trailer, however, required the help of a human as the AI-bot didn’t have editing capabilities. “Our system could select the moments, but it’s not an editor. We partnered with a resident IBM filmmaker to arrange and edit each of the moments together into a comprehensive trailer.”
An average time of ten to thirty days is required for the creation of a movie trailer. The editing team manually analyses every moment of the movie that could become a part of the trailer. The eligible candidates are then stitched together in such a way that it gives an overview of the movie. Watson’s involvement shrunk down the whole process to a matter of around 24 hours.
“Reducing the time of a process from weeks to hours –that is the true power of AI.”

The combination of machine intelligence and human expertise is a powerful one. This research investigation is simply the first of many into what we hope will be a promising area of machine and human creativity. We don’t have the only solution for this challenge, but we’re excited about pushing the possibilities of how AI can augment the expertise and creativity of individuals.

Source: IBM,fossbytes

IBM’s Watson Artificial Intelligence discovered a rare illness in a woman suffering from leukaemia

Japanese Doctors Use AI To Detect Rare Leukaemia

Artificial Intelligence (AI), which is looked upon as a threat to humans, as they are rumoured to take place of humans in factories, industries, etc. in the coming years turned saviour for a patient suffering from leukaemia. Yes, you heard it right!

A team of Japanese doctors turned to IBM’s AI system, Watson for help after the treatment for an 60-year-old woman suffering from leukaemia proved unsuccessful. The AI was successfully able to find out that she actually suffered from a different, rare form of leukaemia, as the disease had gone undetected using conventional methods by the doctors.

Arinobu Tojo, a member of the medical team, told Efe news on Friday that the University of Tokyo’s Institute of Medical Science has successfully used the new method of diagnosis, which includes a computer programme capable of studying a huge volume of medical data.

Watson, which has been jointly developed by the US’ IBM and other firms, looked at the woman’s genetic information and compared it to 20 million clinical oncology studies. It later determined that the patient had an exceedingly rare form of leukaemia and recommended a different treatment which was successful.

Originally, the woman had been diagnosed with, and treated for, acute myeloid leukaemia; however, she failed to respond to the traditional treatment methods, which confounded doctors.

The conventional method of diagnosis for different types of leukaemia is based on an evaluation by a team of medical specialists after studying the genetic information of patients as well as the clinical studies available; an enormous task owing to the huge amount of data to be gone through.

Satoru Miyano, a Professor at the University of Tokyo’s Institute of Medical Science, points out that this is proof enough of the ability that AI likely has in the coming years, “to change the world.”

This is the nation’s first case of an AI saving someone’s life, emphasizing that this is “the most practical application in the field of medical and health care for artificial intelligence,” added Seiji Yamada, of the National Institute of Informatics and chairman of the Japanese Society for Artificial Intelligence.

What was remarkable was that the AI was able to diagnose the condition in just 10 minutes. Whether we would be able to see AI as a regular feature in the hospital in the coming years only time will tell.

SOURCES: TECHWORM

Google Uses AI To Cut Energy Used To Cool Its Data Centers

Google uses AI to cool data centers, save energy

Data centers are a large group of networked computer servers typically used by organizations for the remote storage, processing, or distribution of large amounts of data. They power most of our day-to-day life services, apps and systems that we depend upon. However, running thousands of hard drives, processors, networking equipment and magnetic tapes takes a real toll on the grid, which results in poor energy efficiency in data centers. To make things worse, all of that equipment also needs a powerful cooling system to keep it running.

However, Google has found a way to ease that problem. For the last few months, Google’s artificial intelligence (AI) division, DeepMind, has been using machine-learning algorithm at its two datacentres, which has helped the search giant reduce the energy used for data center cooling by 40% and overall energy usage by 15% in power usage efficiency (PUE).

“We accomplished this by taking the historical data that had already been collected by thousands of sensors within the data center – data such as temperatures, power, pump speeds, setpoints, etc. – and using it to train an ensemble of deep neural networks. Since our objective was to improve data center energy efficiency, we trained the neural networks on the average future PUE (Power Usage Effectiveness), which is defined as the ratio of the total building energy usage to the IT energy usage. We then trained two additional ensembles of deep neural networks to predict the future temperature and pressure of the data center over the next hour. The purpose of these predictions is to simulate the recommended actions from the PUE model, to ensure that we do not go beyond any operating constraints,” Google explained in a blogpost.

It resulted in a 40 percent reduction in the amount of energy used for cooling, which was equal to a 15 percent reduction in overall PUE after accounting for electrical losses and other non-cooling inefficiencies. The results were so impressive that Google plans to deploy the system inside all of its data centers by the end of the year.

The use of the AI technology is “a phenomenal step forward” to help cut down energy usage in data centers, DeepMind research engineer Rich Evans and Google data center engineer Jim Gao said on Google’s blog.

According to Evans and Gao, the energy reduction was realized by training DeepMind’s self-learning algorithms to predict how hot data centers were going to get within the next hour. Equipped with that data, the coolers were only able to run at the maximum temperature necessary to keep the servers sufficiently cool. Google’s data centers are used to run such services as Search, YouTube and Gmail.

Using a system of neural networks that zero in on different operating scenarios and limits within the data centers allows DeepMind to make a more efficient and adaptive framework to comprehend data center dynamics and enhance efficiency, according to Evans and Gao.

“The implications are significant for Google’s data centers, given its potential to greatly improve energy efficiency and reduce emissions overall,” Evans and Gao said. “This will also help other companies who run on Google’s cloud to improve their own energy efficiency.”

However, the best thing about the system is it can be deployed in other data centers and environments with no changes, according to Evans. It can even be applied to other domains like the national energy grid, or optimizing water usage.

Google claims that its data centers are already among the most energy-efficient in the world. The company has claimed that its data centers use hardly 50 percent of the energy consumed by most other data centers of comparable size.

“I really think this is just the beginning. There are lots more opportunities to find efficiencies in data centre infrastructure,” said DeepMind’s co-founder, Mustafa Suleyman. “One of the most exciting things is the kind of algorithms we develop are inherently general … that means the same machine learning system should be able to perform well in a wide variety of environments [such as power generation facilities and energy networks].”

With its algorithm being a perfect candidate for many industrial facilities, Suleyman explained that the team is already in talks with interested parties outside of Google.

The team announced it would be releasing a white paper describing its results and how the system was built and implemented in the near future.

SOURCES: TECHWORM