AI is being used across many areas of our lives. When you click on a product on Amazon or search for a certain song on YouTube and get recommendations, that’s AI at work.
An email has also been transformed with ML algorithms; the quick reply features in Gmail and categorized inboxes are driven by artificial intelligence. Even social media applications like Facebook use AI to personalize your experience.
Gmail’s Spam Filter
Gmail’s spam filter uses machine learning to determine what is and isn’t unwanted email. It uses several factors, including sender reputation, to decide whether an email should be moved into the spam folder. It also looks for things like trigger words, such as “cash,” “credit,” or “passwords.” The spam filter is constantly evolving and getting smarter, so if your emails are being sent to the spam folder, ensure they’re not being flagged because of spelling mistakes or poor grammar.
As email continues to evolve, so do the methods used by hackers and spammers to try and get around Google’s filters. But every time you mark an email as spam or not, you’re helping train the system for future attacks and making the world a little bit safer for everyone.
Gmail’s spam filters use classifiers to segregate patterns of abuse and identify them, and ML algorithms learn from these classifiers to prevent similar attacks in the future. Using this technology, Google prevents phishing and spam attacks by rate-limiting or blocking them before they reach their servers.
The music streaming platform’s recommendations are governed by an incredibly complex system of AI algorithms and ML models. The algorithm analyses every song you listen to on Spotify, your playlists and what songs you like and skip. This allows the algorithm to understand what kind of music you like and give you recommendations that match your preferences.
One of the main ways it does this is through collaborative filtering, which takes consumer behavior data and uses it to predict future behavior (Johnson, 2014). It also uses Natural Language Processing which analyses the description and lyrics of songs to identify descriptive words and make recommendations based on those keywords.
However, Spotify’s recommendation system only sometimes hits the mark, especially for new artists who need more likes. This is because the algorithm may bury new music with more popular songs, a known problem for machine learning systems that use collaborative filtering. To counter this, the platform tries to find more unique ways of predicting what you’ll want to hear next.
The Netflix home page is a great example of an effective recommendation algorithm. When creating an account on the streaming service, you must pick a few movie titles you like to jump-start their recommendation system. It then collects granular implicit behavioral data and other relevant information, such as your age, the time of day you watch, and the types of movies and TV shows you enjoy.
As your preferences and viewing habits change, so must the Netflix algorithm. The company’s recommendations are constantly being tweaked using machine learning, a method of AI that can improve results over time.
However, even the best algorithms can become skewed if they are fed correctly or biased data. British mathematician Marcus du Sautoy warns that if the algorithms are not designed properly, they can become incredibly powerful and may make inferences that we would find concerning. As a result, Netflix must be transparent about how its recommendation engine works and allows users to access and control the data they provide. This can help ensure that the algorithm is not making biases based on race, age, or gender.
According to recent artificial intelligence news, Amazon’s Alexa is the brain behind millions of smart home devices that let you control them with voice commands. While it may seem like a trivial feature, it is an early example of AI and machine learning revolutionizing the way we interact with technology.
Google Assistant, Alexa can respond to specific phrasing and even learn to distinguish one user’s tone of voice from another. However, unlike Google Assistant, which has a single wake word that only works on one device, Alexa is available in many different products from Amazon and its partners and on third-party hardware.
It can also be integrated with various apps and third-party services to make it more versatile. For example, if you’re establishing a bedtime or morning routine, you can set an action that includes turning on the TV lights, disabling the alarm clock, and starting an ambient noise machine.
To understand your request, Alexa sends the audio recording of your voice to a cloud service called Amazon’s Alexa Voice Service (AVS). The system then processes the information and responds.
Several automakers have introduced features that move toward partial automation, like BMW’s Traffic Jam Pilot. It allows a driver to relax their attention and let the car accelerate and brake in heavy traffic, but only at speeds below 40 mph. It will alert the driver to shift gears or take over control if needed.
Self-driving cars use machine learning and neural networks to process data from various sensors. The car’s map-building and path-planning systems learn to recognize countless road and environmental conditions, such as traffic lights, curbs, pedestrians, and street signs. The car’s Lidar and video cameras can see and track objects, while GPS, cellular, and radar sensors determine the vehicle’s position and monitor surrounding traffic.
Boosters of autonomous cars claim they’ll save us time and stress by eliminating traffic jams and deadly crashes. They also predict they’ll make cities more livable, bringing people back to their once-favorite places, where honking cars and parking lots no longer spoil the experience. In addition, self-driving vehicles will allow people with disabilities or mental impairments to get around without needing a driver’s license, and they may reduce the cost of health care for elderly citizens by allowing them to attend more doctor appointments.