Artificial Intelligence

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Artificial intelligence (AI) makes it possible for machines to learn from experience, adjust to new inputs and perform human-like tasks. Most AI examples that you hear about today – from chess-playing computers to self-driving cars – rely heavily on deep learning and natural language processing. Using these technologies, computers can be trained to accomplish specific tasks by processing large amounts of data and recognizing patterns in the data.

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Why is Artificial Intelligence important?

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AI is different from hardware-driven, robotic automation. Instead of automating manual tasks, AI performs frequent, high-volume, computerized tasks reliably and without fatigue. For this type of automation, human inquiry is still essential to set up the system and ask the right questions.
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In most cases, AI will not be sold as an individual application. Rather, products you already use will be improved with AI capabilities, much like Siri was added as a feature to a new generation of Apple products. Automation, conversational platforms, bots and smart machines can be combined with large amounts of data to improve many technologies at home and in the workplace, from security intelligence to investment analysis.
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AI finds structure and regularities in data so that the algorithm acquires a skill: The algorithm becomes a classifier or a predictor. So, just as the algorithm can teach itself how to play chess, it can teach itself what product to recommend next online. And the models adapt when given new data. Back propagation is an AI technique that allows the model to adjust, through training and added data, when the first answer is not quite right.
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Building a fraud detection system with five hidden layers was almost impossible a few years ago. All that has changed with incredible computer power and big data. You need lots of data to train deep learning models because they learn directly from the data. The more data you can feed them, the more accurate they become.
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Your interactions with Alexa, Google Search and Google Photos are all based on deep learning – and they keep getting more accurate the more we use them. In the medical field, AI techniques from deep learning, image classification and object recognition can now be used to find cancer on MRIs with the same accuracy as highly trained radiologists.
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When algorithms are self-learning, the data itself can become intellectual property. The answers are in the data; you just have to apply AI to get them out. Since the role of the data is now more important than ever before, it can create a competitive advantage. If you have the best data in a competitive industry, even if everyone is applying similar techniques, the best data will win.
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How Artificial Intelligence works

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Machine learning automates analytical model building. It uses methods from neural networks, statistics, operations research and physics to find hidden insights in data without explicitly being programmed for where to look or what to conclude.
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A neural network is a type of machine learning that is made up of interconnected units (like neurons) that processes information by responding to external inputs, relaying information between each unit. The process requires multiple passes at the data to find connections and derive meaning from undefined data.
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Deep learning uses huge neural networks with many layers of processing units, taking advantage of advances in computing power and improved training techniques to learn complex patterns in large amounts of data. Common applications include image and speech recognition.
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Cognitive computing is a subfield of AI that strives for natural, human interaction with machines. Using AI and cognitive computing, the ultimate goal is for a machine to simulate human processes through the ability to interpret images and speech – and then speak coherently in response.  
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Computer vision relies on pattern recognition and deep learning to recognize what’s in a picture or video. When machines can process, analyze and understand images, they can capture images or videos in real time and interpret their surroundings.
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Natural language processing (NLP) is the ability of computers to analyze, understand and generate human language, including speech. The next stage of NLP is natural language interaction, which allows humans to communicate with computers using normal, everyday language to perform tasks.
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Working together with AI

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Artificial intelligence is not here to replace us. It augments our abilities and makes us better at what we do. Because AI algorithms learn differently than humans, they look at things differently. They can see relationships and patterns that escape us. This human – AI partnership offers many opportunities. It can:

  • Bring analytics to industries and domains where it’s currently underutilized.
  • Improve the performance of existing analytic technologies, like computer vision and time series analysis.
  • Break down economic barriers, including language and translation barriers.
  • Augment existing abilities and make us better at what we do.
  • Give us better vision, better understanding, better memory and much more.

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Our Approach

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We analyze your business to find out which AI model can solve current problems, add value and improve performance.

  • Requirements management plan
  • Development process overview

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We extract, explore, visualize and transform data for use with AI algorithms. AI can analyse information, learn from it and make smart decisions based on previous experience.

  • Data collection
  • Data exploration & cleanup

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We transform data used in the previous step for training and augment it with additional features. This makes machine learning algorithms work more effectively.

  • Normalization & standardization
  • Applying domain knowledge

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We create a custom AI model specifically for the engineering and business needs of your enterprise. Then the system is trained to perform an analysis to find patterns and correlations to create meaningful predictive models.

  • Picking learning tasks
  • Selecting algorithms
  • Cross-validation

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We integrate AI model to the existing systems and processes of your business seamlessly.

  • Deployment
  • Maintenance

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