Artificial Intelligence (AI) is the intelligence exhibited by machines. It means а machine’s ability to perform tasks that usually could only be performed by utilizing the human mind. Writing AI has been a dream for many decades, but today we see some elements everywhere, even in your own pocket.
Today’s top-notch AI includes autonomous vehicles (drones and self-driving cars), medical diagnosis (Watson), creating art (such as texts, music and even poetry), proving mathematical theorems, playing games (Chess, Go and many others), search engines, personal assistants (such as Siri), image recognition in photographs and videos (used by both Facebook and many city surveillance systems), spam filtering, prediction of judicial decisions and targeting online advertisements (Google Analytics). AI is included in more and more areas every day.
When you issue a command to a virtual assistant such as Siri, Alexa, or Cortana, natural language processing technology (NLP) allows the program AI to interpret your speech and respond in everyday language. Apple, Amazon, Google and Microsoft are making great strides in NLP technology, but unfortunately, these tech giants aren’t interested in sharing how they do it. AI helpers are getting better at talking to other apps and services, and they’re starting to pop up on other companies’ hardware, so you’ll no longer need an iPhone, Windows PC, Amazon Echo, or Google Home speaker to interact with them. And it won’t stop at your phone. Gartner predicts, that by the end of 2018, “customer digital assistants” will recognize customers by face and voice across channels. There are many API that are used for developing artificial intelligence assistants and chatbots, such as Api.ai, wit.ai and many others that allow for coding an AI on any level of complexity from a simple chatbot to your own AI assistant that responds to questions, and even to make an AI program that learns new information and can hold a simple conversation.
For developers looking into ways on how to build their own AI, AI platforms are the ideal solution. Like a standard application platform, these tools often provide drag-and-drop functionality with pre-built algorithms and code frameworks to assist in building the application from scratch. The difference between AI platforms and cloud platforms as a service (PaaS) products is that the former provides the ability to add in the machine learning and deep learning libraries and frameworks when constructing the application. AI platforms ultimately give applications an intelligent edge. AI platforms are a mix of open-source and proprietary products, meaning they make possible the creation of an intelligent application with little overhead. However, for those without sufficient development knowledge, these platforms may prove to be challenging, even with the inclusion of drag-and-drop functionality for beginners.
Many languages are used to develop AI systems:
- Python is one of the most popular and widely used due to its simplicity and availability of different libraries from which to pool the resources.
- Java is great for algorithms.
- C++ is useful for hardware communication.
Google Cloud Prediction API is a platform for building machine learning solutions. As the name suggests, it is based on Google’s cloud platforms, so all of the tools are cloud-based. It is widely used to implement such elements of AI to your apps as customer sentiment analysis, spam detection, recommendation systems and more. Cloud prediction is free for a limited number of predictions and training per month. Usage fees and some other things to keep in mind in terms of pricing are explained here.
Microsoft Azure Machine Learning offers a cloud-based analytics platform created to make machine learning more applicable and easy to use for businesses. It is a part of the Cortana Intelligence Suite that allows for utilizing the system using natural language and speech to interact with data and analysis.
With the evolution of artificial intelligence becoming more mainstream, one may ask about the impact on automation and, in particular, on the more knowledge-intensive tasks such as the discipline of predictive analytics. Increasing levels of automation that continue to replace labor may result in outsourcing becoming a moot point as technology costs become far inferior to using lower-cost labor from third-world countries. But what skills will artificial intelligence replace within the data scientist’s arsenal? In theory, choosing the right mathematical algorithm becomes obsolete as the machine determines the right technique. The machine, through its artificial intelligence algorithms, outputs the solution that can be immediately applied to a given business problem.
In AI, we hear new concepts such as deep learning, which utilize the mathematics of neural nets. Keep in mind that neural nets are not new to predictive analytics and have been used by practitioners for the last twenty years. In fact, much of the more recent developments in AI have been about enhancing the developments of neural net algorithms, and the literature has provided many examples that have yielded superior results to what was developed fifteen years ago. This is particularly relevant in the areas of image and voice recognition.
Deep learning algorithms differ from machine learning algorithms specifically because they use artificial neural networks to make their predictions and decisions, and do not necessarily require human training. With artificial neural networks, elaborate algorithms can make decisions in a way similar to the human brain. However, the decisions are made on a smaller scale because replicating the number of neural connections in the human brain is currently impossible. Deep learning can be broken down into the subcategories of image recognition (computer vision), natural language processing (NLP), and voice recognition. Image recognition algorithms allow applications to learn specific images pixel by pixel; the most common usage of an image recognition algorithm may be Facebook’s ability to recognize the faces of your friends when tagging them in a photo. NLP has the ability to consume human language in its natural form, which allows a machine to easily understand simple commands and speech by the user. NLP is widely used in applications such as iPhone’s Siri or Microsoft’s Cortana in Windows products. Each of these subcategories utilizes artificial neural networks and rely on the networks’ deep layers of neural connections for an increased level of learning.
While each of these AI solutions or algorithms offer very advanced capabilities, the commonality of their usage is only increasing. Soon, all applications will contain some form of machine or deep learning, so those interested in developing their own application may find the knowledge of these libraries and frameworks somewhat of a necessity.
Artificial General Intelligence
Artificial general intelligence (AGI), sometimes called strong AI, is the intelligence of a machine that could successfully perform any intellectual task that a human being can. While many philosophers debate the nature of the ability of the machine to perceive itself and actually posses machine consciousness, we can agree on some aspects of what constitutes “True AI”:
- Reason, use strategy, solve puzzles and make judgments under uncertainty
- Represent knowledge, including commonsense knowledge
- Communicate in natural language
- Integrate all these skills toward common goals
The computing power has been the biggest obstacle in the creation of such AIs, but it may change very soon.
A problem that would take current computing systems billions of years to solve, quantum computers will solve in seconds. With a quantum computer that is 100 million times more powerful than classical computers, we could do things such as simulate your entire body digitally and create custom drugs specifically for any ailment you might have. But a primary application for quantum computing is artificial intelligence. Lockheed Martin plans to use its D-Wave quantum computer to test autopilot software that is currently too complex for classical computers, and Google is using a quantum computer to design software that can distinguish cars from landmarks. Google also sees its quantum computing project as a firm step toward making a self-improving AI. It is still not understood how to develop artificial intelligence software for such machines, and simply how to create an AI as a whole, but it will be clear when the quantum computers will be available on the market.
Our company has experience in creating solutions that include AI elements. We have implemented voice and image recognition systems, intelligent system management, and have experience in cognitive computing and embedded systems using Linux Embedded, MS Cognitive Services and IBM Watson. Feel free to contact our specialists to get more information.