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Archive for the ‘artificial intelligence’ Category

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

What is the hottest trend in artificial intelligence right now? Machine Learning is the right answer! Thanks to technological advances and emerging frameworks, Machine Learning may soon hit the mainstream. Because of new computing technologies, Machine Learning today is not like Machine Learning of the past. While many Machine Learning algorithms have been around for a long time, the ability to automatically apply complex mathematical calculations to big data – over and over, faster and faster – is a recent development. Every single day it’s become clear that Machine Learning is already forcing massive changes in the way companies operate. Every Fortune 500 company is already running more efficiently — and making more money — because of Machine Learning. But how this “phenomenon” helps business bring money and attract new and new customers?

Problems that can be easily solved using ML

Every single business some time or other can face with definite problems. But there are some kinds of business problems Machine Learning can prevent if not handle at all:

Email spam filters
Some spam filtering can be done by rules (IE: by overtly blocking IP addresses known explicitly for spam), but much of the filtering is contextual based on the inbox content relevant for each specific user. Lots of email volume and lots of user’s marking “spam” (labeling the data) makes for a good supervised learning problem.

Speech recognition
There is no single combination of sounds to specifically signal human speech, and individual pronunciations differ widely – Machine Learning can identify patterns of speech and help to convert speech to text. Nuance Communications (maker of Dragon Dictation) is among the better known speech recognition companies today.

Face detection
It’s incredibly difficult to write a set of “rules” to allow machines to detect faces (consider all the different skin colors, angles of view, hair / facial hair, etc), but an algorithm can be trained to detect faces, like those used at Facebook. Many tools for facial detection and recognition are open source.

Credit card purchase fraud detection
Like email spam filters, only a small portion of fraud detection can be done using concrete rules. New fraud methods are constantly being used, and systems must adapt to detect these patterns in real time, coaxing out the common signals associated with fraud.

Product / music / movie recommendation
Each person’s preferences are different, and preferences change over time. Companies like Amazon, Netflix and Spotify use ratings and engagement from a huge volume of items (products, songs, etc) to predict what any given user might want to buy, watch, or listen to next.

Here is enumerated not all but just a few problems that can be solved. And with the course of time this list will only expand.

Industries that already use ML in action

Most industries working with large amounts of data have recognized the value of Machine Learning technology. The adoption of Machine Learning is likely to be diverse and across a range of industries, including retail, automotive, financial services, health care, and etc. By gleaning insights from this data – often in real time – organizations are able to work more efficiently or gain an advantage over competitors. In some cases, it will help transform the way companies interact with customers.

Retail industry
Machine Learning could completely reshape the retail customer experience. The improved ability to use facial recognition as a customer identification tool is being applied in new ways by companies such as Amazon at its Amazon Go stores or through its Alexa platform. Amazon Go removes the need for checkouts through the use of computer vision, sensor fusion, and deep or Machine Learning, and it’s expected that many shopping centers and retailers will start to explore similar options this year.

Financial services
Banks and other businesses in the financial industry use Machine Learning technology for two key purposes: to identify important insights in data, and prevent fraud. The insights can identify investment opportunities, or help investors know when to trade. Data mining can also identify clients with high-risk profiles, or use cyber surveillance to pinpoint warning signs of fraud.

Health care
Machine Learning is a fast-growing trend in the health care industry, thanks to the advent of wearable devices and sensors that can use data to assess a patient’s health in real time. The technology can also help medical experts analyze data to identify trends or red flags that may lead to improved diagnoses and treatment. Machine Learning can be used to understand risk factors for disease in large populations. For instance, Medecision company developed an algorithm that is able to identify eight variables to predict avoidable hospitalizations in diabetes patients.

Oil and gas
Finding new energy sources. Analyzing minerals in the ground. Predicting refinery sensor failure. Streamlining oil distribution to make it more efficient and cost-effective, and many others thing that you can do using ML. For example ExxonMobil, the largest publicly traded international oil and gas company, uses technology and innovation to help meet the world’s growing energy needs. Exxon Mobil’s Corporate Strategic Research (CSR) laboratory is a powerhouse in energy research focusing on fundamental science that can lead to technologies having a direct impact on solving our biggest energy challenges.

Government
Government agencies such as public safety and utilities have a particular need for Machine Learning since they have multiple sources of data that can be mined for insights. Analyzing sensor data, for example, identifies ways to increase efficiency and save money. Machine Learning can also help detect fraud and minimize identity theft. Chicago’s Department of Public Health is early adopter. It used to identify children with dangerous levels of lead in their bodies through blood tests and then cleanse their homes of lead paint. Now it tries to spot vulnerable youngsters before they are poisoned.

Marketing and sales
Websites recommending items you might like based on previous purchases are using Machine Learning to analyze your buying history – and promote other items you’d be interested in. This ability to capture data, analyze it and use it to personalize a shopping experience (or implement a marketing campaign) is the future of retail. PayPal, for example, is using Machine Learning to fight money laundering. The company has tools that compare millions of transactions and can precisely distinguish between legitimate and fraudulent transactions between buyers and sellers.

Transportation
Analyzing data to identify patterns and trends is key to the transportation industry, which relies on making routes more efficient and predicting potential problems to increase profitability. The data analysis and modeling aspects of Machine Learning are important tools to delivery companies, public transportation and other transportation organizations. In some cases, mathematical models are used to optimize shipping routes. By honing in on excessive driving routes, drivers can see a reduction of nearly one mile of driving every day. For a company like UPS, a reduction of one mile per day per driver would equal a savings of as much as $50 million a year in fuel.

Have you ever worked with ML? Was it useful for your business? Or maybe you are still thinking about whether it costs to implement Machine Learning in your business? Will it be relevant and defensibly? If you have an answer on at least one question – share with me your experience. We will be happy to discuss it in comments. But if you don’t have an answer, always remember – Big companies are investing in Machine Learning not because it’s a fad or because it makes them seem cutting edge. They invest because they’ve seen positive ROI. And that’s why innovation will continue.

 

Yuliya Poshva

Business Development Manager

E-mail: yuliya.poshva@altabel.com
Skype: juliaposhva
LI Profile: Yuliya Poshva

 

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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