Amazing ways Google uses Artificial Intelligence
As we all know google is everywhere
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.
What is Artificial Intelligence?
Artificial intelligence (AI) is wide-ranging branch of computer science concerned with building smart machines capable of performing tasks that typically require human intelligence. AI is an interdisciplinary science with multiple approaches, but advancements in machine learning and deep learning are creating a paradigm shift in virtually every sector of the tech industry. we can say that Machine Learning is a part of AI by this we can provide computer or a machine to learn .Process of learning by machine is called Maachine Learninng
Machine learning is essentially using algorithms to calculate trends, value, or other characteristics of specific things based on historical data.Google has even declared itself a machine learning-first company.
How Google is using Machine Learning ?
“ Hmm ….Song stuck in your Head? Dont worry” , just Hum to google
Over a billion people every day use Google Search, the company said. Despite having worked on the software for more than 20 years, every day there are 15 percent of queries that Google has never seen before. To keep up with the constantly changing queries, sources of information and ways to present results, Google needs to tap the power of AI. Today, the company announced a set of updates that can make Search easier to use, and the most notable of these is a new “Hum to Search” feature that’s avalable today.
It’s like Shazam or Soundhound for song identification, except you don’t need to have the music playing. You can just hum, whistle or sing 10 to 15 seconds of your earworm after tapping the mic icon in the search bar on your phone and saying “what’s this song” or “search a song.” You can also ask the Assistant “what is this song?”
Then, the machine learning algorithm will identify potential matches, even if you weren’t using the right pitch. Results will be delivered based on the tune you hummed and you can pick the best match.
How Machine Learning in Search Works:
RankBrain
RankBrain is Google’s best-known machine learning tool, and it is used to help Google better understand the connections between different concepts and entities to ensure that Google’s users see the best possible search results. In the beginning, RankBrain was given a fundamental understanding of entities, which is a concept that is singular, well-defined and distinguishable — like movie names or dates. Then, the program was tasked with training itself to recognize unknown entities on the web as well as training itself to understand relationships between entities and search requests, so it could scour Google’s index for the best results.
The beauty of RankBrain, and machine learning in general, is that the algorithm is improving search results every hour of every day. That is especially important for the average web user, who wants nothing more or less than exactly what they are looking for. However, RankBrain is also useful for website owners and creators, who can relax on keywords and metadata and focus more on producing high-quality, valuable content that users are searching for.
RankBrain uses machine learning to:
- Continuously learn about the connectedness of entities and their relationships.
- Understand when words are synonyms and when they are not (replace and fix may be synonyms in this case but they wouldn’t be if I was querying “how to fix my car”).
- Instruct other portions of the algorithm to produce the correct SERP.
In its first iteration, RankBrain was tested on queries Google had not encountered before. This makes perfect sense and is a great test.
If RankBrain can improve results for queries that likely weren’t optimized for and will involve a mix of old and new entities and services a grouping of users who were likely getting lackluster results to begin with then it should be deployed globally.
Spam
If you use Gmail, or pretty much any other email system, you also are seeing machine learning at work.
According to Google, they are now blocking 99.9% of all spam and phishing emails with a false-positive rate of only 0.05%.
They’re doing this using the same core technique — give the machine learning system some data and let it go.
If one was to manually program in all the permutations that would yield a 99.9% success rate in spam filtering and adjust on the fly for new techniques it would be an onerous task if at all possible.
When they did things this way they sat at a 97% success rate with 1% of false
Enter machine learning — set it up with all the spam messages you can positively confirm, let it build a model around what similarities they have, enter in some new messages and give it a reward for successfully selecting spam messages on its own and over time (and not a lot of it) it will learn far more signals and react far faster than a human ever could.
So How Does Machine Learning Work?
A common machine learning model follows the following sequence:
- Give the system a set of known data. That is, a set of data with a large array of possible variables connected to a known positive or negative result. This is used to train the system and give it a starting point. Basically, it now understands how to recognize and weigh factors based on past data to produce a positive result.
- Set up a reward for success. Once the system is conditioned with the starting data it is then fed new data but without the known positive or negative result. The system does not know the relationships of a new entity or whether an email is spam or not. When it selects correctly it is given a reward though clearly not a chocolate bar. An example would be to give the system a reward value with the goal of hitting the highest number possible. Each time it selects the right answer this score is added to.
- Set it loose. Once the success metrics are high enough to surpass existing systems or meet another threshold the machine learning system can be integrated with the algorithm as a whole.
Thank you for reading :)
