Both pictures and video stream recognition systems including One-shot Image Recognition. Strong competence in creating computer vision apps and integrating image-recognition ai-solutions.
Text generating systems, recommendation systems based on text recognition, classification and clusterization algorithms. Our work covers areas such as sentence understanding, syntactic parsing and tagging, sentiment analysis, and models of text and visual scenes.
Create prediction and classification models using both traditional and deep learning. Develop deep configuration architecture and unsupervised feature learning mechanism that can handle a large amount of data.
AI can rapidly analyze large amounts of data and detect abnormalities, thus it is very well suited for monitoring applications, such as detecting credit-card fraud, cybersecurity intrusions, early warning signs of illnesses, or important changes in the environment.
AI can extract valuable insights from large datasets, often referred to as data mining.
In particular, because AI uses dynamic models that learn and adapt from data, it is
very effective at uncovering abstract patterns and revealing novel insights.
AI can forecast or model trends, thereby enabling systems to predict and personalize responses. These types of applications, such as Netflix’s recommendation algorithm, analyze users’ viewing histories and suggest new titles that they might like.
Until recently, most data analytics was focused on structured data. Because AI can learn and identify patterns, it can interpret unstructured data, such as images and text.
AI allows autonomous systems to engage directly with the physical environment. It enables robotic systems that can navigate and manipulate the world around them.
AI can allow humans to interact more easily with computer systems. With AI computer systems can respond to speech, gestures, and even facial expressions.
AI can automatically coordinate compli- cated machine-to-machine interactions e.g. data center computing activity and environmental conditions control.
Autonomous indoor farming system can use networked sensors and machine learning to constantly monitor a farm’s environment and plants condition and adjust lighting, temperature, humidity, water, and soil nutrient levels or notify the farmer about the plant’s diseases e.g. mold, bacteria, or insect damage. The system learns from historical data and refines its predictions to maximize the farm’s productivity.
A start-up called One Concern has developed a system that uses AI to model a town’s buildings, based on factors such as building age, density, and construction materials, and predict how they would react to seismic activity.70 If an earthquake hits, One Concern can plug in the new seismic data to estimate which areas are likely the most damaged.71 Using these predictions, first responders can prioritize disaster response efforts to target the hardest hit areas after an earthquake.
Researchers at Stanford University have developed a machine-learning system that can predict M- and X-class solar flares, which produce dangerously high levels of radiation that could harm airline passengers, damage power grids, and disrupt communication satellites.80 The system analyzes data about the sun, such as the topology of its magnetic field and its atmosphere, and can identify regions of the sun likely to produce solar flares.81 More advanced warning of when and where a solar flare could occur will allow airlines, power grid operators, and others to take precautionary measures to avoid danger.
The ability to predict the stock market is, as any Wall Street trader will tell you, a license to print money. So stock analytical systems are of no small interest to anyone, who develops machine learning. System of this kind work by ingesting large quantities of financial news along with minute-by-minute stock price data, and then using the former to figure out how to predict the latter. The algorithm buys, or shorts, every stock it believes will move more than 1% of its current price in the next 20 minutes - and it never holds a stock for longer. The results are quite impressive: such an algorithm performs at least on parity with the 10 top rated funds (S&P top 500 rating).
Autonomous recycling system uses a robotic arm, an array of sensors, and AI to identify recyclable items in waste and separate them for recycling, removing the need for manual sorting. It analyzes trash on a conveyer belt using 3-D scanning, spectrometer analysis, and other methods to determine what a piece of trash is made of, and, if recyclable, a robotic arm will pick it up and move it to a separate container.
Virtual-assistant service can analyze employee calendars and emails to automatically schedule meetings and adjust calendar appointments. Users can copy the virtual assistant in emails when they want to set up meetings, and it will analyze email text to determine the topic and time of a meeting, determine if there are any conflicts, and automatically schedule calendar appointments. The app can also search for and add relevant phone numbers, reschedule meetings by conversing with users, and learn users’ preferences over time.
We invent the next generation of computer infrastructure through innovations in software design, enabling new applications for mobile, cloud and data-intensive computing.
Our work ranges from basic research in AI algorithms to key applications in banking, retail, insurance and automotive industries. A distinguishing feature of the NGenics Group is our effective combination of sophisticated and deep modeling and data analysis with innovative probabilistic, machine learning, and deep learning approaches.
INFO at NGENICS.AI