Autonomic computing is simply the ability of a computer to automatically manage itself through adaptive technologies that further computing capabilities and reduce the time required by computer professionals to solve system and other maintenance problems such as software updates.
This is another development in technology which is set to become prominent in tomorrow’s networks. Autonomic computing refers to a network’s self-managing functionality. Typically, in the event of failures or faults, it means a network’s ability to self-heal.
The autonomic network can easily detect and isolate network faults while leaving other network pieces untouched. Besides, without human intervention, these networks can quickly correct and ‘heal’ the defective hardware. In smart grids, autonomic networks are typical where a fault can be quickly isolated and the network cured without leading to a major outage in the electrical grid.
In dynamic scenarios where the responses may be unclear and vague, cognitive computation with the use of computerized simulations to mimic the human thought process. The term is strongly related with Watson, the cognitive machine system of IBM. Cognitive computing overlaps with AI and includes several of the same fundamental technology, including expert systems, neural networks, robotics and virtual reality, to power cognitive software (VR).
IBM’s supercomputer, Watson, an artificial intelligence computer system capable of answering questions asked in natural language, is the most popular technical tool in the field of cognitive computing. Watson is best known in the famous U.S. for trouncing a national champion effectively. Jeopardy, the TV quiz show. What rendered this triumph more impressive was that the supercomputer was able to decode and select the right response to the complexities in natural language.
Following Jeopardy’s popularity, Watson has also been hired by a major U.S. medical insurance firm to identify medical conditions and prescribe patient care plans. Watson would be able to examine 1 million books, or about 200 million data pages. Siri, the speech recognition software on the iPhone, is another well-known example of cognitive computing. Cognitive computing’s old avatar was specialist programs focused on artificial intelligence. This specialist system were inference engines which were focused on laws of knowledge.
5G is the 5th generation mobile network. It is a new global wireless standard after 1G, 2G, 3G, and 4G networks. 5G enables a new kind of network that is designed to connect virtually everyone and everything together including machines, objects, and devices.
5G wireless technology is meant to deliver higher multi-Gbps peak data speeds, ultra-low latency, more reliability, massive network capacity, increased availability, and a more uniform user experience to more users. Higher performance and improved efficiency empower new user experiences and connects new industries.
No one company or person owns 5G, but there are several companies within the mobile ecosystem that are contributing to bringing 5G to life. Qualcomm has played a major role in inventing the many foundational technologies that drive the industry forward and make up 5G, the next wireless standard.
The previous generations of mobile networks are 1G, 2G, 3G, and 4G.
First generation – 1G 1980s: 1G delivered analogy voice.
Second generation – 2G Early 1990s: 2G introduced digital voice (e.g. CDMA- Code Division Multiple Access).
Third generation – 3G Early 2000s: 3G brought mobile data (e.g. CDMA2000).
Fourth generation – 4G LTE 2010s: 4G LTE ushered in the era of mobile broadband.
1G, 2G, 3G, and 4G all led to 5G, which is designed to provide more connectivity than was ever available before.
5G is a unified, more capable air interface. It has been designed with an extended capacity to enable next-generation user experiences, empower new deployment models and deliver new services.
With high speeds, superior reliability and negligible latency, 5G will expand the mobile ecosystem into new realms. 5G will impact every industry, making safer transportation, remote healthcare, precision agriculture, digitized logistics — and more — a reality.
Predictive analytics is a form of advanced analytics that helps to get information from existing data sets in order to dictate patterns and forecast future outcomes and trends. Marketing, insurance companies and financial services have also been users of predictive analytics, as have large search engine and online services providers.
When ML drives predictive analytics in e-commerce, it can reverse-engineer customer behavior to drive enhanced experiences by analyzing data generated from multiple sources (including websites, mobile apps, social media, and the Internet of Things) in real time.
How does this work? Let’s take a look.
a. Predictive Analytics Enables Personalized Customer Journeys
The insights gained from customer data (past behavior, expectations, and desires) will help you tailor online shopping experiences to perfectly fit the profile of each customer. Delivering Personalization at such a granular level can boost brand loyalty and improve customer retention rates.
For example, almost 80% of what’s watched on Netflix is based on recommendations. This approach has helped the company save as much as a billion dollars in value thanks to customer retention.
Online retail giant Amazon has been effectively leveraging its comprehensive collaborative-filtering engine for years in order to make accurate recommendations. This approach has helped the company up-sell and cross-sell successfully.
If we take Amazon’s category of DVDs, for example, recommendations of similar movies purchased by other customers have helped generate as much as 35% of the sales annually.
Now the company wants to take this to the next level by leveraging predictive analytics to ship products to customers even before they buy anything. While anticipatory shipping sounds like something out of a science fiction movie, it has the potential to enhance the brand’s free one-day Prime delivery service.
In this scenario, Amazon will push its one-day shipping offer with popular items and categories like beach towels, beauty products, cleaning supplies, and similar.
b. Predictive Analytics Helps Improve Customer Lifetime Value
When you have the insights allowing you to make highly accurate predictions, you can have a better understanding of the Customer Lifetime Value, or CLV. For example, if a customer spends $20 a year on your products for ten years, then their CLV will be $200.
You can calculate this based on past purchasing behaviour and the products they are forecasted to buy in the future.
With the help of big data and smart algorithms, you can extend the CLV by doing the following:
Enhancing customer experiences
Funnelling traffic from social media platforms
Recommending products that complement past purchases
Segmenting your email and SMS subscription lists
When you’re alerted to the early signs of customer dissatisfaction, you can set your customer retention protocols in motion and reduce the churn rate.
c. Predictive Analytics Allows Brands to Augment and Refine Products
When you have an in-depth understanding of your customers’ expectations, your business will be well-placed to adapt your offering to meet the demands of your target market. Essentially, this will mean centralizing your resources on the most highly-demanded SKUs while balancing your investments in market research and new product development.
You can apply predictive analytics in all the areas mentioned above, so that your efforts complement each other for maximum effect. But none of these benefits can be possible without oceans of data and highly sophisticated intelligent algorithms.
The good news is that you don’t have to be a multinational corporation like Amazon to afford predictive analytics technologies. Even small and medium-sized enterprises can access smart insights to get ready for Black Friday and beyond.
Edge computing is transforming the way data is being handled, processed, and delivered from millions of devices around the world. The explosive growth of internet-connected devices – the IoT – along with new applications that require real-time computing power, continues to drive edge-computing systems.
Faster networking technologies, such as 5G wireless, are allowing for edge computing systems to accelerate the creation or support of real-time applications, such as video processing and analytics, self-driving cars, artificial intelligence and robotics, to name a few. While early goals of edge computing were to address the costs of bandwidth for data traveling long distances because of the growth of IoT-generated data, the rise of real-time applications that need processing at the edge will drive the technology ahead.
Gartner defines edge computing as “a part of a distributed computing topology in which information processing is located close to the edge – where things and people produce or consume that information.”
At its basic level, edge computing brings computation and data storage closer to the devices where it’s being gathered, rather than relying on a central location that can be thousands of miles away. This is done so that data, especially real-time data, does not suffer latency issues that can affect an application’s performance. In addition, companies can save money by having the processing done locally, reducing the amount of data that needs to be processed in a centralized or cloud-based location.
Edge computing was developed due to the exponential growth of IoT devices, which connect to the internet for either receiving information from the cloud or delivering data back to the cloud. And many IoT devices generate enormous amounts of data during the course of their operations.
Here you have a list of 10 examples of Artificial Intelligence that one is likely to encounter on a daily basis.
Google Maps and Ride-Hailing Applications
One doesn’t have to put much thought into traveling to a new destination anymore. Instead of having to rely on confusing address directions, you can now simply open up the handy map application on your phone and type in your destination.
So how does the application know the exact directions, the optimal route, and even road barriers and traffic congestions? Not too long ago, only GPS (satellite-based navigation) was used as guidance for commuting. But now, artificial intelligence is being incorporated to give users a much more enhanced experience in regards to their specific surroundings.
Via machine learning, the app algorithm remembers the edges of the buildings that have been fed into the system after the staff has manually identified them. This allows the addition of clear visuals of buildings on the map. Another feature is the quality of recognizing and understanding handwritten house numbers which help commuters reach the exact house they were looking for. Places that lack formal street signs can also be identified with their outlines or handwritten labels.
The application has been taught to understand and identify traffic. Thus, it recommends the best route that avoids roadblocks and congestion. The AI-based algorithm also tells users the exact distance and time they will reach their destination as it has been taught to calculate this based on traffic conditions. Users can also view the pictures of their locations before getting there.
So by employing a similar AI technology, various ride-hailing applications have also come into existence. So whenever you book a cab from an app by putting your location on a map, this is how it works.
Face Detection and Recognition
Using virtual filters on our face when taking pictures and using face ID for unlocking our phones are two applications of AI that are now part of our daily lives. The former incorporates face detection meaning any human face is identified. The latter uses face recognition through which a specific face is recognized.
How does this work?
Intelligent machines often match – and sometimes go even above and beyond! – human capabilities. Human babies start recognizing facial features like eyes, nose, lips and face shapes. But that is not all there is to a face. There are a plethora of factors that make human faces unique. Smart machines are taught to identify facial coordinates (x, y, w, and h; that make a square around the face as an area of interest), landmarks (eyes, nose etc), and alignment (geometric structures). This takes the human ability to recognize faces several notches up.
Face recognition is also used for surveillance and security by government facilities or at airports. For example, Gatwick Airport, London, uses face recognition cameras as ID checks before allowing passengers to board the plane.
Text Editors or Auto correct
When you’re typing out documents, there are inbuilt or downloadable auto-correcting tools for editors that check for spelling mistakes, grammar, readability, and plagiarism depending on their complexity level.
It must have taken you a while to learn your language before you became fluent in it. Similarly, artificially intelligent algorithms also use machine learning, deep learning, and natural language processing to identify incorrect usage of language and suggest corrections.
Linguists and computer scientists work together to teach machines grammar, just like you were taught at school. Machines are fed with copious amounts of high-quality language data, organized in such a manner that machines can understand it. So when you use even a single comma incorrectly, the editor will mark it red and prompt suggestions.
The next time you have a language editor check your document, know that you are using one of the many examples of artificial intelligence.
Search and Recommendation Algorithms
When you want to watch your favorite movies or listen to songs or perhaps shop online, have you noticed that the items suggested to you are perfectly aligned with your interests? This is the beauty of AI.
These smart recommendation systems learn your behavior and interests from your online activities and offer you similar content. The personalized experience is made possible by continuous training. The data is collected at the frontend (from the user), stored as big data and analyzed through machine learning and deep learning. It is then able to predict your preferences by recommendations that keep you entertained without having to search any further.
Similarly, the optimized search engine experience is another example of artificial intelligence. Usually, our top search results have the answer we’re looking for. How does that happen?
Quality controlling algorithms are fed with data to recognize high-quality content over SEO-spammed, poor content. This helps make an ascending order of search results based on quality for the best user experience.
As search engines are made up of codes, natural language processing technology helps these applications to understand humans. In fact, they are also able to predict what a human wants to ask by compiling top-ranked searches and predicting their queries when they start to type.
New features such as voice search and image search are also constantly being programmed into machines. If you want to find a song that is playing at a mall, you can simply hold your phone up to it, and a music-identifying app will tell you what it is within seconds. After sifting through the rich database of songs, the machine will also tell you all the details related to that song.
As a customer, getting queries answered can be time-consuming. An artificially intelligent solution to this is the use of algorithms to train machines to cater to customers via chat-bots. This enables machines to answer FAQs, and take and track orders.
Chat-bots are taught to impersonate the conversational styles of customer representatives through natural language processing (NLP). Advanced chat-bots no longer require specific formats of inputs (e.g. yes/no questions). They can answer complex questions requiring detailed responses. They will give the impression of a customer representative when, in fact, they are just another example of artificial intelligence.
If you give a bad rating for the response you get, the bot will identify the mistake it made and correct it the next time, ensuring maximum customer satisfaction.
When we have our hands full, we often resort to ordering digital assistants to perform tasks on our behalf. When you are driving with a cup of coffee in one hand, you might ask the assistant to call your mom. The assistant, for example, Siri will access your contacts, identify the word “Mom”, and call the number.
Interestingly, Siri is old news, as it is an example of a lower-tier model that can only respond when spoken to and not give complex answers. The latest digital assistants are well-versed in human language and incorporate advanced NLP and ML. They understand complex command inputs and give satisfactory outputs. They have adaptive capabilities that can analyze your preferences, schedules, and habits. This allows them to systematize, organize and plan things for you in the form of reminders, prompts and schedules.
The advent of social media provided a new narrative to the world with excessive freedom of speech. However, this brought some societal evils such as cybercrime, cyberbullying, and hate speech. Various social media applications are using the support of AI to control these problems and provide users with other entertaining features.
AI algorithms can spot and swiftly take down posts containing hate speech a lot faster than humans could. This is made possible through their ability to identify hate keywords, phrases, and symbols in different languages. These have been fed into the system, which has the additional capability to add neologisms to its dictionary. The neural network architecture of deep learning is an important component of this process.
Emojis have become the best way to represent various emotions. This digital language is also understood by AI technology as it can understand the connotation of a certain piece of text and prompt the correct emoji as part of predictive text.
Social media, being a great example of artificial intelligence, also has the ability to understand the sort of content a user resonates with and suggests similar content to them. The facial recognition feature is also utilized in social media accounts, helping people tag their friends through automatic suggestions. Smart filters can identify and automatically weed out spam or unwanted messages. Smart replies are another feature users can enjoy.
Some future plans of the social media industry include using artificial intelligence to identify mental health problems such as suicidal tendencies through analyzing the content posted and consumed. This can be forwarded to mental health doctors.
Having to run to the bank for every transaction can be a hectic errand. Good news! Banks are now leveraging artificial intelligence to facilitate customers by simplifying payment processes.
Artificial intelligence has made it possible to deposit cheque from the comfort of your home. AI is proficient in deciphering handwriting, making online cheque processing practicable.
The way fraud can be detected by observing users’ credit card spending patterns is also an example of artificial intelligence. For example, the algorithms know what kind of products User X buys, when and from where they are bought, and in what price bracket they fall. When there is some unusual activity that does not fit in with the user profile, the system instantly alerts user X.
Saving energy is the biggest reason people consider upgrading from a programmable thermostat, and the new Nest Thermostat can help find ways to save that aren’t possible with your traditional one.
Quick Schedule (found in the Home app) lets you set a custom temperature at different times and on different days, and it even offers suggested pre-set temperatures that balance comfort and energy saving. You can adjust your settings anytime from the app.
With Savings Finder, Nest Thermostat is constantly looking for small optimizations that will help you save energy in your home. It proactively suggests small tweaks to your schedule that you can accept using the Home app. For example, it might suggest a small change to your sleep temperature to help aid sleep while saving you more on energy.
Finally, the Nest Thermostat can help you avoid heating or cooling an empty house. It uses Soli technology for motion sensing and your phone’s location to check if you’ve left the house and automatically sets itself to an Eco temperature so you don’t waste energy when you’re not there.
Tesla’s mission is to accelerate the world’s transition to sustainable energy. Tesla was founded in 2003 by a group of engineers who wanted to prove that people didn’t need to compromise to drive electric – that electric vehicles can be better, quicker and more fun to drive than gasoline cars.
If you don’t own a Tesla, you have no idea what you’re missing. This is quite possibly one of the best cars ever made. Not only for the fact that it’s received so many accolades, but because of its predictive capabilities, self-driving features and sheer technological “coolness.” Anyone that’s into technology and cars needs to own a Tesla, and these vehicles are only getting smarter and smarter thanks to their over-the-air updates.
The highest impact of IoT has been on the Smart Devices. As the name itself suggests smart devices are the devices that have grown smart, that have the ability to do things that even only the great minds are capable of. Google speaker for an example is a smart device that acts upon voice recognition. As the user commands the device via voice the device performs the action. It can do everything a normal speaker can and in fact more just with a means of voice. Similarly, Motion sensor light is a device that decides to whether turn on or off a light just by sensing the presence of a person in the room.