Top Machine Learning Applications
Top Machine Learning Applications
Machine learning is a hot topic at the moment. It has got people coming up with ideas about how a robot can teach itself to solve all their problems.
It has already had a big impact on modern society, from its use in recommendation engines to Apple’s Siri virtual assistant.
Here are some of the best applications of machine learning being used today:
Virtual Personal Assistants
We have just mentioned Siri, but Amazon’s Alexa and Google’s own version are other examples of virtual personal assistants being used every day. They all have a similar purpose: to find information and assist in answering queries.
These applications use machine learning techniques to collect information based on how they have been previously used in the past. They may also reach out to other phone/tablet/etc applications to find the answers.
The results are then saved for future references so if they are asked to set an alarm the following morning, they will have a good idea of the time.
Social Media Applications
Companies like Facebook, Instagram and Twitter using machine learning for a whole number of reasons, ranging from personalised ads to tailoring a news feed.
Further examples include:
When a picture is uploaded to Facebook or Instagram, their algorithms will be able identify who is in the image. They scan the picture for similar features to previous photos and match them to people from the friends list.
Machine learning processes are also used when suggesting to add a friend or someone to follow. They see a list of mutual friends and followers and come up with suggestions based on similar connections. This extends to suggestions for liking a certain group or following a hashtag.
These social media sites will also monitor the pages visited and profiles/chats visited frequently and come up with suggestions based on the activity.
Machine learning plays a large role in translating one language to another. The best varieties understand the context of what is being said and adapt.
The technology behind the translation tool is called ‘machine translation’. It has enabled the world to interact with people from all corners of the world, without it life would not be as easy as it is now.
It has provided a sort of confidence to travellers and business associates to safely venture into foreign lands with the conviction that language will no longer be a barrier.
Applications may combine language translation with a voice recognition system to save time on typing.
Email clients detect which emails are considered spam and those that are not by machine learning processes. Filters are continually updated to ensure the right messages are coming through.
The program identifies the frequency of emails sent from a provider and bases whether they are considered spam on previous interactions with the email address or the company it is being sent from.
A lot of spam mail contains malware and viruses. However, the majority of malware coding are related to previously filtered versions.
Machine learning processes enable security systems to detect similar coding patterns and identify the malware.
Artificial intelligence in healthcare is helping to save lives every day. Machine learning is being used to reduce waiting times for patients, so they can get the help they need.
Some of the factors that are involved in producing the algorithms include patient records, notes and doctor and nurse availability. The systems scan through this information can come up with the best treatment options available.
One study used computer assisted diagnosis (CAD)when to review the early mammography scans of women who later developed breast cancer, and the computer spotted 52% of the cancers as much as a year before the women were officially diagnosed.
Geo-locations use computer vision methods to deliver warnings to drivers such as traffic.
Maps are evolving to show the best route to get to a destination. Depending on the time of day and the likelihood of running into a rush hour jam, systems learn how to use this data to come up the best way to travel.
GPS navigation applications use current locations and velocities which are then saved and stored at a central server for managing traffic. This data is then used to build a map of current traffic.
While this helps in preventing the traffic and does congestion analysis, the underlying problem is that there are less number of cars that are equipped with GPS. Machine learning in such scenarios helps to estimate the regions where congestion can be found on daily experiences.
Perhaps the most famous use of machine learning, Google and its competitors are constantly improving what the search engine understands. Every time a search is made, Google monitors how the user reacts to the results.
Clicking on the first result indicates that the search was a success. On the other hand, clicking on to the second page or entering a new search into the bar indicates that the results didn’t satisfy the query.
The machine learning program can pick up on this and will try to provide better results next time.
Many retailers use recommenders to analyse activity on an online store to suggest items similar to those already viewed. The activity is compared to all the other users to determine what the customer is likely to buy next.
The more products viewed, the more data these programs capture and are able to provide more accurate and better suggestions. They are also intelligent enough to realise if someone is purchasing particular products at certain times of the year of if they are being bought as gifts.
Recommendation engines are now also used as part of streaming services, like Netflix and Spotify to bring music and TV suggestions.