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Posts Tagged ‘natural language processing

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
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altabel

Altabel Group

Professional Software Development

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

The demand for healthcare services is growing at rapid pace due to constantly increasing number of people with chronic diseases. These days approximately every one of two individuals has one or more chronic diseases, and one of four has two or more chronic conditions. At the same time, there are more medical information today about different diseases and their treatment options than ever before.
 

According to IBM, healthcare data doubles every 2 years. It is also calculated that doctors would have to read 29 hours each workday to keep up with new professional insights. Obviously while dealing with this huge information flow, doctors don’t have enough capacities to decide how appropriate an option might be for a specific patient.

Additionally, the most expensive part of healthcare is the human resources, which adds to the supply-and-demand issues. I guess no one will doubt the fact that professional healthcare is costly.

These insights bring up several questions. How can we benefit from explosion of information in healthcare industry? Is it possible to cut the costs for people who seek healthcare treatment without sacrificing the quality of such services? Or even improving it? How do we find a balance after all?

The answer lies in two words: cognitive computing. It is a system that can handle massive amounts of unstructured data to enable a new class of data interpretation and learning systems. Cognitive systems process information by comparing it to a teaching set of data. So that the more data such a system can analyze, the more it learns, and therefore the more accurate it becomes with the course of time. To mimic the way the human brain works cognitive systems use data mining, pattern recognition and natural language processing.

The main advantage of these machine-learning systems is their ability to find patterns in datasets too large and complex for human brains to embrace. For doctors this means assistance of paramount importance in keeping track of records and making accurate clinical decisions. IDC predicts that by 2018 somewhat 30 percent of healthcare systems will be running cognitive analytics against patient data and real-world evidence to personalize treatment regiments. What’s more, IDC projects that during the same year physicians will tap cognitive solutions for nearly half of cancer patients and, as a result, will reduce costs and mortality rates by 10 percent.

For patients the ability of cognitive computing to act as an advisor and give an additional opinion allows an extra level of assurance in the service provided by the healthcare sector. Eventually the patients will have more confidence in the service they are receiving. Besides, involving cognitive computing into healthcare means availability of remote check-ups, including areas with relatively little healthcare provision. It is predicted that in the U.S., for example, in the nearest future 40% of primary care encounters will be delivered virtually, which will be possible thanks to cognitive systems.

Summing up, cognitive computing can help:

  • Healthcare specialists to manage all the data that is available to make more precise conclusions over the patients’ conditions
  • Patients by advising, and providing answers to the questions they have
  • Decrease costs for healthcare services

As data becomes more complex and diversified, cognitive computing will have an incredible impact on the healthcare industry.

In conclusion, let me give you one single real-life example. Watson (famous IBM cognitive system used to diagnose patients) was able to determine a rare form of leukemia in an old woman, while oncologists at the University of Tokyo had puzzled for about a year over her illness. After analyzing 20 million research papers Watson came up with the proper diagnosis. It took the system no more than ten minutes. Impressive, isn’t it?

 

alexandra-presniatsova

Alexandra Presniatsova

Business Development Manager

E-mail: Alex.Presniatsova@altabel.com
Skype: alex.presniatsova
LI Profile: Alexandra Presniatsova

 

altabel

Altabel Group

Professional Software Development

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


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