10 Artificial Intelligence Technologies That Dominating In 2018

1. Natural Language Generation

Natural language generation is an AI sub-discipline that converts data into text, enabling computers to communicate ideas with perfect accuracy.
It is used in customer service to generate reports and market summaries and is offered by companies like Attivio, Automated Insights, Cambridge Semantics, Digital Reasoning, Lucidworks, Narrative Science, SAS, and Yseop.

2. Speech recognition

Siri is just one of the systems that can understand you.
Every day, more and more systems are created that can transcribe human language, reaching hundreds of thousands through voice-response interactive systems and mobile apps.
tecnologías-inteligencia-artificial-adext-ai-4.jpg
Companies offering speech recognition services include NICE, Nuance Communications, OpenText and Verint Systems.

3. Virtual Agents

A virtual agent is nothing more than a computer agent or program capable of interacting with humans. 
The most common example of this kind of technology is chatbots. 
Virtual agents are currently being used for customer service and support and as smart home managers.
artificial-intelligence-technologies-chatbots
Some of the companies that provide virtual agents include Amazon, Apple, Artificial Solutions, Assist AI, Creative Virtual, Google, IBM, IPsoft, Microsoft and Satisfy.

4. Machine Learning Platforms

These days, computers can also easily learn, and they can be incredibly intelligent!
Machine learning (ML) is a subdiscipline of computer science and a branch of AI. Its goal is to develop techniques that allow computers to learn.
By providing algorithms, APIs (application programming interface), development and training tools, big data, applications and other machines, ML platforms are gaining more and more traction every day.
They are currently mainly being used for prediction and classification.
Some of the companies selling ML platforms include Amazon, Fractal Analytics, Google, H2O.ai, Microsoft, SAS, Skytree.
tecnologia-inteligencia-artificial-adext.png

5. AI-Optimized Hardware

AI technology makes hardware much friendlier.
How? 
Through new graphics and central processing units and processing devices specifically designed and structured to execute AI-oriented tasks.
And if you haven’t seen them already, expect the imminent appearance and wide acceptance of AI-optimized silicon chips that can be inserted right into your portable devices and elsewhere.
You can get access to this technology through Alluviate, Cray, Google, IBM, Intel, and Nvidia.

6. Decision Management

Intelligent machines are capable of introducing rules and logic to AI systems so you can use them for initial setup/training, ongoing maintenance, and tuning.
artificial-intelligence-technologies-decision-management
Decision management has already been incorporated into a variety of corporate applications to assist and execute the automated decision, making your business as profitable as possible.
Check out Advanced Systems Concepts, Informatica, Maana, Pegasystems, and UiPath for additional options.

7. Deep Learning Platforms

Deep learning platforms use a unique form of ML that involves artificial neural circuits with various abstraction layers that can mimic the human brain, processing data and creating patterns for decision making.
It is currently mainly being used to recognize patterns and classify applications that are only compatible with large-scale data sets.
Deep Instinct, Ersatz Labs, Fluid AI, MathWorks, Peltarion, Saffron Technology, and Sentient Technologies all have deep learning options worthy of exploring.

8. Biometrics

This technology can identify, measure and analyze human behavior and physical aspects of the body’s structure and form.
artificial-intelligence-technologies-image-recognition
It allows for more natural interactions between humans and machines, including interactions related to touch, image, speech, and body language recognition, and is big within the market research field.
3VR, Affectiva, Agnitio, FaceFirst, Sensory, Synqera and Tahzoo are all biometrics companies working hard to develop this area.

9. Robotic Processes Automation

Robotic processes automation uses scripts and methods that mimic and automate human tasks to support corporate processes. 
It is particularly useful for situations when hiring humans for a specific job or task is too expensive or inefficient.
Cómo complementar tu estrategia de marketing digital con ADEXT.png
It’s a solution that lets you make the most of your human talent and move employees into more strategic and creative positions, so their actions can really make an impact on the company's growth.
Advanced Systems Concepts, Automation Anywhere, Blue Prism, UiPath, and WorkFusion are other examples of robotic processes automation companies.

10. Text Analytics & NLP (Natural Language Processing)

This technology uses text analytics to understand the structure of sentences, as well as their meaning and intention, through statistical methods and ML.
Text analytics and NLP are currently being used for security systems and fraud detection.
They are also being used by a vast array of automated assistants and apps to extract unstructured data.
Some of the service providers and suppliers of these technologies include Basis Technology, Coveo, Expert System, Indico, Knime, Lexalytics, Linguamatics, Mindbreeze, Sinequa, Stratifyd, and Synapsify.

Comments

Post a Comment

Popular posts from this blog

The 10 Machine Learning Algorithms Which Every Machine Learning Enthusiast must learn

Image
It is no doubt that the sub-field of machine learning / artificial intelligence has increasingly gained more popularity in the past couple of years. As Big Data is the hottest trend in the tech industry at the moment, machine learning is incredibly powerful to make predictions or calculated suggestions based on large amounts of data. Some of the most common examples of machine learning are Netflix’s algorithms to make movie suggestions based on movies you have watched in the past or Amazon’s algorithms that recommend books based on books you have bought before. So if you want to learn more about machine learning, how do you start?  Machine learning algorithms can be divided into 3 broad categories — supervised learning, unsupervised learning, and reinforcement learning.Supervised learning is useful in cases where a property ( label ) is available for a certain dataset ( training set ), but is missing and needs to be predicted for other instances. Unsupervised learni...
Read more

A Simple Way to Understand Machine Learning vs Deep Learning

Image
A Simple Way to Understand Machine Learning vs Deep Learning Understanding how today’s artificial intelligence works might seem overwhelming, but it really boils down to two concepts you probably have heard of before: “machine learning” and “deep learning”. Neither are brand new ideas, but the way they’re used to describe intelligent machines seems to constantly evolve. Machine learning and deep learning are how Netflix knows what you might want to watch next, or how Facebook can recognize your friends’ face in a photo, or  how a support agent can figure out if you’ll be satisfied with your customer service . So what are these buzzwords that still dominate the conversations about artificial intelligence, and how exactly are they different? And what do they mean for customer service?   What is machine learning? Here’s a basic definition of machine learning:  “Algorithms that parse data, learn from that data, and then apply what they’ve learned to make i...
Read more

Microsoft creates AI that can read a document and answer questions about it as well as a person

Image
Microsoft researchers have created technology that uses artificial intelligence to read a document and answer questions about it about as well as a human. It’s a major milestone in the push to have search engines such as Bing and intelligent assistants such as Cortana interact with people and provide information in more natural ways, much like people communicate with each other. A team at  Microsoft Research Asia  reached the human parity milestone using the Stanford Question Answering Dataset, known among researchers as  SQuAD . It’s a machine reading comprehension dataset that is made up of questions about a set of Wikipedia articles. According to the SQuAD leaderboard, on Jan. 3, Microsoft submitted a model that reached the score of 82.650 on the exact match portion. The human performance on the same set of questions and answers is 82.304. On Jan. 5, researchers with the Chinese e-commerce company Alibaba submitted a score of 82.440, also about the same as a human. Th...
Read more