How MNC’s getting benefits from AI and ML
GOOGLE :
The Amazing Ways Google Uses Artificial Intelligence And Satellite Data To Prevent Illegal Fishing
Google services such as its image search and translation tools use sophisticated machine learning which allow computers to see, listen and speak in much the same way as human do. Google uses machine learning in its Nest “smart” thermostat products — by analyzing how the devices are used in households they become better at predicting when and how their owners want their homes to be heated, helping to cut down on wasted energy.
This results in the broadcasting of around 22 million data points every day, and Google engineers found that by applying machine learning to this data they were able to identify the reason any vessel is at sea — whether it is a transport ferry, container ship, leisure vessel or fishing boat. Google’s image recognition algorithms were trained to recognize how to spot solar arrays in satellite images. This system was quickly put to use by the city of San Jose in California as part of an initiative to identify locations where 1 gigawatt of solar energy could be generated from new panels.
Both of these initiatives are great examples of how machine learning — powered by publicly available datasets — are enabling new solutions to problems of the modern age. As more data becomes available, and computers become increasingly powerful.
2. WIKIPEDIA :
The Amazing Ways How Wikipedia Uses Artificial Intelligence
The Wikipedia community, the free encyclopedia that is built from a model of openly editable content, is notorious for its toxicity. The issue was so bad that the number of active contributors or editors — those that made one edit per month — had fallen by 40 percent during an eight-year period. Even though there’s not one solution to combat this issue, Wikimedia Foundation, the nonprofit that supports Wikipedia, decided to use artificial intelligence to learn more about the problem and consider ways to combat it.
A team within Google Brain taught software to summarize info on web pages and write a Wikipedia-style article. It turns out text summarization is more difficult than most of us thought. Google Brain’s efforts to get a machine to summarize content is slightly better than previous attempts, but there is still work to be done before a machine can write with the cadence and flair humans can. It turns out we’re not quite ready to have a machine automatically generate Wikipedia entries, but there are efforts underway to get us there.
While the use cases for artificial intelligence in the operations of Wikipedia are still being optimized, machines can undoubtedly help the organization analyze the vast amount of data they generate daily. Better information and analysis can help Wikipedia create successful strategies to troubleshoot negativity from its community and recruitment issues for its contributors.
3. JAGUAR :
How Jaguar Land Rover Is Getting Ready For The 4th Industrial Revolution: AI & Autonomous Cars
As the United Kingdom’s largest automobile manufacturer and investor in research and development in the UK manufacturing sector, Jaguar Land Rover is the combination of two iconic British car brands — Jaguar that features luxury sports cars and sedans and Land Rover, maker of premium all-wheel-drive vehicles. These brands began in the middle of the 20th century and gained a reputation for innovation. The company plans to continue the tradition of innovation as they pave the way to the future through AI and machine learning investments and applications.
Jaguar Land Rover hopes to perfect is autonomous off-road driving tech on its vehicles. The company recognizes the autonomous vehicles are inevitable, but they want to ensure that the capabilities and performance levels current JLR customers expect are still available in an autonomous future. Through Project Cortex, technology such as video, radar, light detection, distance sensing and more are combined to allow the car to adapt to any environment. Due to machine learning, the vehicles will learn from experiences and be able to get better over time. The goal with Project Cortex is to have autonomous technology capable of levels four and five automation — four being able to operate in specific environments and five being able to operate without any human intervention.
4. TESLA :
The Amazing Ways Tesla Is Using Artificial Intelligence And Big Data
Tesla has become a household name as a leader and pioneer in the electric vehicle market, but it also manufactures and sells advanced battery and solar panel technology.
Tesla effectively crowdsouces its data from all of its vehicles as well as their drivers, with internal as well as external sensors which can pick up information about a driver’s hand placement on the instruments and how they are operating them. As well as helping Tesla to refine its systems, this data holds tremendous value in its own right.
The data is used to generate highly data-dense maps showing everything from the average increase in traffic speed over a stretch of road, to the location of hazards which cause drivers to take action. Machine learning in the cloud takes care of educating the entire fleet, while at an individual car level, edge computing decides what action the car needs to take right now. A third level of decision-making also exists, with cars able to form networks with other Tesla vehicles nearby in order to share local information and insights. In a near future scenario where autonomous cars are widespread, these networks will most likely also interface with cars from other manufacturers as well as other systems such as traffic cameras, road-based sensors or mobile phones.
5. SPOTIFY :
The Amazing Ways Spotify Uses Big Data, AI And Machine Learning To Drive Business Success
When news broke that Francois Pachet, a French scientist and expert on music composed by AI, joined the Spotify team to “focus on making tools to help artists in their creative process,” not everyone believed that’s ALL that he’d do. You can just imagine how a leader in AI might use his expertise to turn the tables at Spotify to make AI-composed music that would push out artists and their labels. So far, Spotify denies that this will be the case even though this isn’t the first AI feature they launched — AI Duet released earlier this year where listeners could create a duet with a computer.
As Spotify learned in 2015, its community will respond if it feels like it’s taking too many liberties with data. After introducing large-scale changes to its privacy policy, users let the company know they were angry by cancelling subscriptions and taking to social media to express their dismay. This prompted Spotify CEO Daniel Ek to apologize for unclear communication and made it clear any access to personal data would only occur with the permission of the individual.
We might not know today where Spotify will innovate next, but we will be watching. As innovators they will encounter learning experiences and even failures as they use big data, AI and machine learning to drive success. Those are experiences we can all learn from.
