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Posts Tagged ‘deep learning

Artificial Intelligence, or AI, is everywhere these days. From once being a futuristic concept in Hollywood movies, to now touching our daily lives. Artificial intelligence applications, projects and platforms are being developed in every part of the world today. More and more of them successfully escape lab life and strike mainstream trends, appearing in mass products, online tools and open-source APIs. The market for AI is ripe and research estimates put it at around $5 billion by 2020.

But did you know that artificial intelligence actually debuted in 1956? Some people believed robots and AI machines would be doing the work of humans by the mid-1970s. Of course, that didn’t happen. What happened instead was that funding dried up and a period called “The AI Winter” began. That ostensibly lasted into the 2000s, when IBM’s Watson showed a lot of interest in artificial intelligence again.

And now in 2017 you may find AI examples everywhere — in robotics, healthcare, business and everyday life, in the cloud and on your mobile device.
 

 
And one of the most promising areas for AI is in mobile. The growth of artificial intelligence is driving a whole new class of mobile app possibilities.
 
What makes mobile an ideal platform for AI?

AI has transformed how we interact with our smartphones. Thanks to the advances in the fields of Natural Language Processing, Deep Learning and Machine Learning, we have been able to make chat-bot interfaces, which are much more natural and convenient.

AI capabilities are being built into mobile apps of all kinds, making them contextually aware of user behavior and making each app session more valuable than the last, increasing overall retention rates. With the ability to quickly analyze massive amount of consumer behavior and data, mobile devices with artificial intelligence applications can recognize a person the way humans recognize other people — by individual characteristics.

It’s impossible to enumerate all of the applications we will see for mobile devices capable of performing sophisticated perceptual tasks involving vision, speech, or other sensory input. But they are likely to be found in every industry. Please find a few well-known examples below.

SIRI is one of the most famous AI applications. It’s personal assistant software for Apple devices, which works as an intelligent knowledge guide to recommend, answer questions and delegate requests to other connected web services.

GOOGLE NOW is another intelligent personal assistant that goes as a part of the Google app available for Android and iOS. The app allows Google to pull all the synced information from all Google services you use and your location history for making you recommendations and alerts in the form of different Google Now Cards: Activity summary, Boarding pass, Events, Flights, Location reminders, Parking location, TV and many other.

CORTANA is the Microsoft’s intelligent personal assistant initially designed for Windows Phone. Cortana software reacts to a user’s voice and accomplishes limited commands, answers questions using the information from the browser installed, works as a secretary by scheduling events, locating necessary files and opening the apps needed.

ALEXA is the voice service created by Amazon for Amazon Echo intelligent speaker. Alexa uses natural language processing algorithms to adapt to natural voice of the user. The more a user interacts with Alexa the more it evolves and gets smarter, delivering higher quality answers to a user’s questions.

KINECT is an AI-based motion controller and a motion sensing technology by Microsoft that is used in Xbox One and Xbox 360 game consoles. Kinect analyzes natural user interface and reacts to voice commands and gestures. Kinect technology for non-gaming purposes including healthcare, retail industry, military and robotics.

 
How Will Mobile AI Impact Businesses?

There are three ways AI can help your business: virtual assistance, insights generation and manual process automation.

Virtual assistance is something a small business can start using right away. You already use Siri on a daily basis. A virtual assistant can assist with customer service tasks like scheduling meetings or answering simple and repetitive customer questions.

AI can be helpful with generation of insights. We are collecting massive amounts of data on customers, but it is pointless if it is not in a usable form. AI can transform that data into practical insights and learn from it, allowing AI to adapt to market behavior changes.

Automation of manual process is taking place very much like the industrial revolution when machines replaced people. AI is using smart algorithms replacing routine and often time-consuming tasks such as compiling reports and researching topics.

Major players in the technology industry already proved the success of AI mobile apps. With new advancements in technology and shifting consumer demands, AI mobile app development is the next big thing for enterprises:

  • Bank of America, for instance, is currently developing Erica, a “virtual assistant” that can give financial advice based on a customer’s spending patterns through the bank’s app.
  • Facebook, for example, has integrated chat-bots into its Messenger app for seamless interactions for businesses.
  • Uber uses this technology to provide the best route to its driver by learning from previous trips along the same route taken by their drivers.
  • It’s also used by YouTube to recommend you similar music.
  • Retail giants such as eBay and Amazon use it for product recommendations.
  • Starbucks announced a new AI-powered mobile app called “My Starbucks Barista.” Users simply tell the app what they want, and it places the order for them.
  • Similarly, Taco Bell released the TacoBot, which doesn’t just take orders, but also recommends menu items and answers questions.

The benefits of AI technology across the enterprise are far from being fully realized, so it stands to reason that there’s huge interest in AI among businesses at the moment. By 2018 the world’s top 200 companies will be exploiting what they call “intelligent apps” — it’s only a matter of time.

And if you still think AI is out of your apps’ reach, consider that you might not be aware that you’re already using AI in your company.

Thanks for reading! If you have any questions or comments, you are welcome with them!

 

Victoria Sazonchik

Victoria Sazonchik

Business Development Manager

E-mail: victoria.sazonchik@altabel.com
Skype: victoria_sazonchik
LI Profile: Victoria Sazonchik

 

altabel

Altabel Group

Professional Software Development

E-mail: contact@altabel.com
www.altabel.com

Artificial Intelligence, Machine Learning are new buzzwords that are actively discussed in the tech world. Do you remember how our future was described in the movies some time ago: Terminator, Skynet, AI rules the world? General AI machines have remained in the movies and science fiction novels however narrow AI technologies are gradually evolving from the science fiction era to the reality and are already around us. Google uses Machine Learning to filter out spam messages from Gmail. Facebook trained computers to identify specific human faces nearly as accurately as humans do. Deep Learning is used by Netflix and Amazon to decide what you want to watch or buy next.

AI, machine learning, and deep learning are not quite the same thing but these terms are often used haphazardly and interchangeably, and that sometimes leads to some confusion. So let`s see what is the difference between each type of technology.
 
Artificial Intelligence (AI)

Artificial intelligence, which has been around since the 1950s, has seen ebbs and flows in popularity over the last 60+ years. But today, with the recent explosion of big data, high-powered parallel processing, and advanced neural algorithms, we are seeing a renaissance in AI—and companies from Amazon to Facebook to Google are scrambling to take the lead.

AI is the broadest way to think about advanced, computer intelligence. It can refer to anything from a computer program playing a game of chess, to a voice-recognition system like Amazon’s Alexa interpreting and responding to speech. The technology can broadly be categorized into three groups: Narrow AI (that is focused on one narrow task), artificial general intelligence or AGI (a machine with the ability to apply intelligence to any problem, rather than just one specific problem), and superintelligent AI (when its equal to humans or even surpasses them).

Pardoe believes that “we’ve just entered the “Fourth Industrial Revolution”, and while the adoption of AI has just started, the next few years will transform many sectors.
 
Machine learning

Machine learning is one subfield of AI. Or let`s say it`s the field of AI which today is showing the most promise at providing tools that industry and society can use to drive change. The core principle here is that machines take data and “learn” for themselves. Unlike hand-coding a software program with specific instructions to complete a task, ML allows a system to learn to recognize patterns on its own and make predictions.
 

 
Here are some of the popular machine learning methods:

-supervised learning: the “trainer” will present the computer with certain rules that connect an input (an object’s feature, like “smooth,” for example) with an output (the object itself, like a marble), and the algorithm learns by comparing its actual output with correct outputs to find errors. Supervised learning is commonly used in applications where historical data predicts likely future events. For example, it can anticipate when credit card transactions are likely to be fraudulent or which insurance customer is likely to file a claim.

-unsupervised learning: the computer is given inputs and is left alone to discover patterns. The goal is to explore the data and find some structure within. Unsupervised learning works well on transactional data. For example, it can identify segments of customers with similar attributes who can then be treated similarly in marketing campaigns.

-reinforcement learning: the algorithm discovers through trial and error which actions yield the greatest rewards. This type of learning has three primary components: the agent (the learner or decision maker, for instance, the driverless car), the environment (everything the agent interacts with, for instance the road) and actions (what the agent can do).
 
Deep Learning

Deep learning is a brunch of Machine Learning, let`s see it as the cutting-edge of the cutting-edge. It uses some ML techniques to solve real-world problems by tapping into neural networks that simulate human decision-making.

Deep Learning involves feeding a computer system with a lot of data, which it can use to make decisions about other data. This data is fed through neural networks. These networks are logical constructions which ask a series of binary true/false questions, or extract a numerical value, of every bit of data which pass through them, and classify it according to the answers received.

Text-based searches, fraud detection, spam detection, handwriting recognition, image search, speech recognition, Street View detection, and translation are all tasks that can be performed through deep learning. Deep Learning is used by Google in its voice and image recognition algorithms, by Netflix and Amazon to decide what you want to watch or buy next, and by researchers at MIT to predict the future.

The machine revolution has certainly started and the AI revolution is sure to pave the way for some significant changes in our lives. Machines will gradually improve, slowly replacing jobs that require repetitious behavior. But what happens when one day the machines become smarter than us?

 

Anna Kozik

Business Development Manager

E-mail: Anna.Kozik@altabel.com
Skype: kozik_anna
LI Profile: Anna Kozik

 

altabel

Altabel Group

Professional Software Development

E-mail: contact@altabel.com
www.altabel.com


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