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Better Designed Data Science

Artificial intelligence, or Ai for short, is computer programming that learns and adapts. It can’t solve every problem, but its potential to improve our lives is profound.

Problem Solving Technology

Ai is not based on a static base of code, instead, it is a constantly evolving set of systems designed to identify, sort, and present the data that is most likely to meet the needs of people at that specific time, based on a multitude of variables.

True Ai processes include learning (the acquisition of information and rules for using the information), reasoning (using rules to reach approximate or definite conclusions) and self-correction.


Chat and Voice based Ai allows people to converse with a machine, rather than a person.


Assessing large data-sets to find patterns, and spot anomalies is now very much a reality across all industries.


Sophisticated machine-vision algorithms allow us to look at pictures or objects and make inferences.


Feed enough data into an algorithm, and we can start to predict what the future might hold.

The stats

Ai is on the rise.

of enterprises are using Ai as of today, but it's rising rapidly..
of digitally mature organisations have a defined Ai strategy.
of people prefer using chatbots to humans.
Of Business leaders believe AI is “business advantage.

How we approach Ai.

Research – What is the problem you’re trying to solve?

Design – How should it feel when you engage with the solution?

Experiment – How far can this technology go for your problem?


How do Us classify types of Ai by need?

We have a unique way of planning and classifying Ai. We define as programmes in the following four ways;

  • Supportive Ai: Systems that are able to intelligently retreive information on request. Such as search, chatbots or expert systems. These systems are also able to learn and evolve over-time when more questions are asked, and more data is a assimilated.
  • Service Ai: Systems that are able to take requests, and perform actions on behalf of someone. For instance, a system that is able to retrieve someones bank-balance on request (supportive) and then give proactive advice on how to better manage money, and budget, based on the data is observes. Taking this one step further, the machine can also make the changes required to help the person get better.
    Predictive Ai: A predictive analytics model can look at a specific set of defined data inputs, and support some kind of decision using machine learning. In business, it may lead to higher sales of a product or increased lift on a promotional effort, or simply help someone make a better decision by predicting various outputs.
  • Perceptual Ai: An autonomous agent that is able to operate on an owner’s behalf but without any interference of that ownership entity. An Intelligent agent will carry out some set of operations on behalf of a business or person, or another program, with a degree of independence or autonomy, and in so doing, employ some knowledge or representation of the desired goals and outcomes.

What is Machine Learning?

Machine learning is a subset of Ai.

The theory is simple, machines take data and ‘learn’ for themselves. It is currently the most promising tool in the Ai pool for businesses. Machine learning systems can quickly apply knowledge and training from large datasets to excel at facial recognition, speech recognition, object recognition, translation, and many other tasks. Machine learning allows a system to learn to recognise patterns on its own and make predictions, contrary to hand-coding a software program with specific instructions to complete a task.

While Deep Blue and DeepMind are both types of Ai, Deep Blue was rule-based, dependent on programming — so it was not a form of machine learning. DeepMind, on the other hand — beat the world champion in Go by training itself on a large data set of expert moves.

That is, all machine learning counts as Ai, but not all Ai counts as machine learning.

Click here to find out more about Machine Learning.

What is Deep Learning?

Deep learning is a subfield of machine learning, which is a vibrant research area in artificial intelligence, or Ai.

Deep artificial neural networks are a set of algorithms reaching new levels of accuracy for many important problems, such as image recognition, sound recognition, recommender systems, etc.

It uses some machine learning techniques to solve real-world problems by tapping into neural networks that simulate human decision-making. Deep learning can be costly and requires huge datasets to train itself. This is because there are a huge number of parameters that need to be understood by a learning algorithm, which can primarily yield a lot of false-positives. For example, a deep learning algorithm could be trained to ‘learn’ how a dog looks like. It would take an enormous dataset of images for it to understand the minor details that distinguish a dog from a wolf or a fox.

Deep learning is part of DeepMind’s notorious AlphaGo algorithm, which beat the former world champion Lee Sedol in 4 out of 5 games of Go using deep learning in early 2016. Google said, “the way the deep learning system worked was by combining Monte-Carlo tree search with deep neural networks that have been trained by supervised learning, from human expert games, and by reinforcement learning from games of self-play.”

What is the difference between Machine Learning and Deep Learning?

Machine learning and deep learning fall under the topic of artificial intelligence. Machine learning describes a process of computers acquiring knowledge on their own. Deep learning has become the most common method of machine learning: This involves a computer acquiring its knowledge using an algorithm, which is used to analyze large volumes of data, and learning to draw conclusions based on this data. Here’s a simple example: Let’s say we want a system’s algorithm to learn how to identify stop signs. We show it one million pictures of stop signs; it accumulates experience as a result. On this basis, the system is subsequently able to identify a stop sign when it appears.

Where is AI being used already?

Thanks to artificial intelligence, image recognition systems in cars are already able to identify street signs today, or sort vacation photos in our smartphones according to certain motifs. Language assistants understand our spoken questions and facilitate the search for information. AI algorithms are able to understand what music we like to listen to and suggest suitable songs. When these algorithms are installed in medical devices, they can identify the symptoms of illness and determine the right treatment methods.

What can AI do – and what is it not able to do?

Analysing images, evaluating data, identifying illnesses – when it comes to these tasks, artificial intelligence is in part already superior to the human brain. It also works without becoming fatigued and is able to respond within a fraction of a second. Thanks to these characteristics, AI is used in areas such as autonomous driving, driver assistance systems, and industrial applications. Collaborative robots, for example, can be trained in new tasks using sample data and machine learning. The limits of AI systems lie in direct interactions with people. Currently, they do not yet have any emotions, empathy, or social intelligence.

How is AI going to change the way we work?

According to AI expert Michael Chui of the McKinsey Global Institute, half of all activities in the working world could already be automated using artificial intelligence today. Yet there will be no mass layoffs. That is because only five percent of occupations can be handled entirely by AI. So companies are still going to need people in the future. However, they will increasingly let machines take care of monotonous or dangerous activities, so that employees will focus on interacting with other people or carrying out creative tasks instead.

Who is conducting artificial intelligence research?

AI systems are being developed by numerous companies, start-ups, and research institutions around the world. We’d like to mention two researchers from Germany as examples: Jürgen Schmidhuber is considered the father of modern artificial intelligence. The neural networks he developed with his team are found in three billion smartphones today, and are also used by Google, Apple, and Facebook. Meanwhile, Bernhard Schölkopf is head of the Max Planck Institute for Intelligent Systems in Tübingen, and numbers among the world’s leading scientists in the area of machine learning. Researcher and entrepreneur Oren Etzioni is also highly renowned in the field of AI. He’s the head of the Allen Institute for Artificial Intelligence in Seattle established by Microsoft co-founder Paul Allen.

The Distraction of True Ai

Because of media influences, a lot of companies are focusing the conversation around true Artificial Intelligence, and whilst this is important for some problems, it’s also a very academic way of approaching problems-to-be-solved. We approach tasks in a Narrow way.

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