Data Modelling & Predictive Analysis
Embecosm can assist you in applying the techniques of artificial intelligence to identify underlying trends and patterns in your data. These techniques can range from the foundational models of classical statistics, to more advanced techniques like Bayesian Networks, to entirely custom complex, computational models. Embecosm can help you best leverage your data and models not just to identify trends and patterns in the data, but to make predictions about future events.
A common task in artificial intelligence is optimisation, finding the best solution to a given problem among many alternatives, perhaps subject to certain criteria. This is a very large field with many different available approaches depending on the specifics of the problem. Embecosm possesses the expertise to help you solve these problems, even in the most difficult of circumstances.
The Hard Cases
Often, in the context of artificial intelligence or data analytics, you will hear discussion about the application of various techniques to huge datasets (“Big Data”). While this is a challenging and interesting field in it’s own right, a more pressing concern for most is bad data, where there is, for example, a paucity of data, some or all of the data is of poor quality, or important parts of it are missing. At Embecosm, we are intimately familiar with the solutions to these hard problems and others, and understand how to get the best out of difficult datasets.
The Latest Advances
While foundational techniques are often at the core of any artificially intelligent solution, artificial intelligence is a rapidly advancing field. Embecosm dedicates considerable effort to staying at the cutting edge, allowing us to leverage the latest advances in our field. This allows us to provide services that take advantage of the best of the state-of-the-art.
Embecosm possesses cutting edge knowledge in the field of machine learning, including data mining, and insight into the way to apply these techniques to datasets of massive sizes (“Big Data”). Embecosm can help you apply the techniques of this growing field and find out how they can transform your business.
A common Machine learning task is supervised learning, or learning by example, in which an algorithm learns the solution to a problem from a set of training examples. These supervised learning tools have become ubiquitous in many fields because of their incredible flexibility: instead of needing to be pre-programmed to understand the problem to be solved, they will learn it from examples. This trait has made them invaluable in many areas in which knowledge of the domain is infeasible or impossible to attain. Embecosm can help you understand these techniques, and how they might be applied to your business.
The classic dream of an artificially intelligent system is one that learns by interacting with its environment. A term adopted to describe systems that learn in such a way is reinforcement learning. Where supervised learning learns from a predefined set of examples, reinforcement learning solutions build (or reinforce) their own list of examples by “interacting” with their environment. This class of algorithm is more suitable for sequential decision problems, which can present difficulties to conventional supervised approaches. Embecosm possesses the expertise to get the best out of this difficult but powerful class of solutions, including the popular classes of evolutionary or genetic algorithms.
Unsupervised Learning and Data Mining
We’ve so far discussed methods that rely on learning by example, but often the creation of a training set of data from which an algorithm can learn is a luxury that circumstances do not afford. In this case, one can bring to bear the power of what are termed unsupervised learning algorithms that do not require training data. One of the most common cases this circumstances arises in is data mining, in which one wishes to extract trends and patterns from data, without knowing in advance what these might be. Embecosm can help you understand when these solutions might be a better alternative to a supervised problem, and how they might be leveraged to achieve your goals.
In the modern age, the capacity of many entities to collect data has exploded, but the capacity to analyse and interpret this self-same data often outstrips the computing power available. This is especially tragic in light of the massive value that can be extracted from these large datasets (“Big Data”). At Embecosm, we understand how to extract the highest value from large datasets, whether your analysis is to be run over a supercomputer or a conventional desktop.
Neural Networks and Deep Learning
Embecosm has strong proficiency in the areas of Neural Networks and Deep Learning. These techniques have revolutionized many fields almost overnight, but without due care and attention the models these techniques create can be anywhere from slow and inefficient to entirely counterproductive. Embecosm has the skills to help you optimally leverage the benefits these models can provide and bring the power of neural networks and deep learning to your business.
Neural Networks and Deep Learning
Artificial Neural Networks have become ubiquitous in businesses of every kind for their flexibility and predictive power as universal function approximators. Deep Learning approaches have taken this a step further by providing techniques that overcome the traditional limits on the length of conventional neural networks (the “vanishing gradient problem”), allowing even more powerful inference. At Embecosm, we understand what makes a good neural network, and can guide you to the solution that best fits your needs.
Despite their merits, neural networks can suffer from a plethora of problems if not carefully handled. Whilst appropriately designed neural networks can leverage specialised hardware to both train and infer quickly, poorly designed networks can be orders of magnitude slower. Furthermore, poor network design or poor training data can result in errors, which can be very difficult to debug because of the opaque “black box” nature of neural network solutions. Our deep knowledge in this area allows us to help customers crack open the black box of neural networks and understand how these issues may arise, and what can be done about them.
Neural Network Bias
A hot topic in the world of Neural Networks and Deep learning is bias, in which the network learns to favour (or disfavour) certain groups. This is especially problematic when it comes to neural networks that interpret human data, where bias based on certain traits is generally completely unacceptable. This is further problematic because of the opaque nature of black box algorithms that make this difficult to diagnose and fix. Embecosm experience in this area allows us to offer our customers insight into how to deal with these problems and come out with the best neural network solution possible.
Dr William Jones
William has a background in research and as the CTO of a successful start-up.
Williams’ research background is in the field of computational neuroscience. His work focused on attacking problems in the fields of consciousness and attention using cutting edge statistical and machine learning techniques, as well as Artificial Neural Networks. This work has given him a long tour through the broad field of data science, as well as a rare viewpoint and strength in the fiends of Artificial Intelligence, Artificial Neural Networks, and Deep Learning.
William was also the founder and CTO of a cryptocurrency, Arweave during the cryptocurrency boom in 2017 to 2018. Arweave is based on its own platform and novel technology called a Blockweave, that allows information to be permanently attached to a blockchain without requiring every user to store each piece of data. Despite the difficulties of building a new platform and protocol from scratch, Arweave flourished and continues to go from strength to strength every day!
Dr Craig Blackmore
Craig has experience in applying a wide range of machine learning techniques, with a specialisation in the application of these to automated compiler tuning. Dr Blackmore pursued these topics during both his Masters degree and his PhD, with his work being the topic of multiple academic publications.
Unsurprisingly, Dr Blackmores skills proved invaluable to the TESRO and MAGEEC projects. More recently, Dr Blackmore has been contributing to the OpenTuner project, an open source compiler tuning framework that makes use of an ensemble of optimisation methods to guide the choice of compiler settings.
Dr Blackmore is a graduate of the University of Bristol, where he achieved First Class Honours in Computer Science and has recently completed his PhD.