The final part of our three part AI Series will focus on the ways you can consider adopting AI for your own systems, how to approach decision-making and the ways in which Bitjam can assist you with the transition.
How Can You Introduce AI into Your Existing Technologies?
At Bitjam, we use the R&D cycle to understand the needs of your business, the technology you have available and the most effective way to introduce AI to your systems. The initial discovery phase helps us to understand your system and business objectives to provide an accurate and “right-first-time” solution.
Bitjam will then explore the possibilities of how machine learning can augment your existing technology, and push the boundaries to see if it can allow diversification of your business.
To prevent overwhelming both you as a business stakeholder and the actual technology available – man and machine, if you like – we always recommend starting with a smaller project within your existing technology-reliant infrastructure that could benefit from cognitive technology.
Using co-production techniques and agile methodology as a tool for collaboration, Bitjam and your business can then consider ways in which AI could be introduced to simply update your system rather than require a complete overhaul that might cause disruption to your workflow.
Bitjam are keen to introduce the idea of AI adaptation to move businesses – both locally and globally – forward in a positive way, and we understand that a huge undertaking both financially and in terms of re-organising massive infrastructure is not usually a viable option. We’re here to lend you our experiences and help you make small yet revolutionary changes to your business.
Call or email us for a chat! Maybe you’re not sure how AI will even fit in with your business, but with so many of our projects now demanding IoT solutions, we’re sure we can help you find the right solution to bring your services up to date.
Last year at Bitjam we launched a small in-house machine learning project to coincide with the ACAVA Studios: Spode Works open studios event. ANNA – a learning algorithm that analyses poetry and audibly delivers it in an old-fashioned regional Potteries dialect – was the result!
Once we had our idea we sat down and had a discussion about the possible complexity of the project. A fully fledged bespoke neural network is quite a lot of work so we decided to try to find some existing neural networks to base our work off of. We found a Recursive Neural Network (RNN) designed to take text input and after a large number of training cycles we then tried to get ANNA to output some meaningful ‘learned’ poetry.
ANNA was a python script based on a simple RNN. We fed in around 200 pages worth of potteries dialect poetry aiming to produce some sort of meaningful poetry. Anna ran through about 500 recursive cycles of the input text per “epoch” of learning for a total of around 30 epochs. An epoch is essentially a single full training cycle.
The main challenge we faced was a shortage of data to feed into the RNN. So the next step for us was to source plenty of potteries dialect based poetry from poets past and present. First we tried to source as much poetry as possible from an online source, the main works we used were by Arnold Bennett. We then tried sourcing further poetry from Wilfred Bloor‘s sons Roger & Ian. Wilfred Bloor wrote over 400 Jabez tales in Potteries dialect (the Jabez character is a countryman living in the shadows of industrial Potteries). Finally, enter, Alan Barrett. Stoke-born writer, storyteller, poet, and actor who helped us train ANNA on the quirks and the peculiarities of the Potteries dialect. Thanks to the kind contributions of these people we managed to collect plenty of poetry that has been fed into ANNA.
The project was a lot of fun – especially since Alan Barrett remained on hand to help us deliver and chat about ANNA on the day of the open event! Our objective was only to take a playful look at neural networks and how they might be trained to learn local dialect. We didn’t exactly expect to achieve it, rather we were curious to see what the results might be. We got ANNA to recreate snippets of prose and dialect, and at times she successfully pieced together and understood some of the dialect.
You can read the original ANNA blog post and interview with Bitjam Senior Developer Liam Mountford, in more detail here.
It’s fascinating to see the ways in which AI has exploded into the mainstream (hopefully we’ll avoid any Terminator-like “SkyNet” disasters!)
In particular, part one of our three part AI Series looks at using AI to crunch data with machine learning – generally described as a computer trained to learn and make recommendations based on data insights. These recommendations provide the ability to make accurate predictions and enhance system performances or personal experiences.
Machine learning has already been adopted in major technology-dependent sectors such as banking, healthcare, aviation and even space exploration:
“(ML) technology is expected to power future space exploration as it can handle huge data volumes, find patterns in planet image datasets, and predict spaceship condition.”
Satellites and space telescopes have already collected a large amount of data. Images provide the main source, but the challenge is how to identify information from the images. ML has become an effective technique for solving these problems.
Machine Learning and AI
Machine learning has progressed since its beginnings in pattern recognition. The theory that computers are able to learn without being programmed to perform tasks has evolved with the rise of AI, and since then research has been focused around proving that computers could actually independently learn from data.
Industries working with large amounts of data recognise the benefits of modern machine learning technology. By mining insights from this data companies are able to work more efficiently or gain an advantage over competitors.
Did you know that Machine learning can be applied to your existing systems in order to modernise and enhance your service and give you a competitive edge? It does not always require a brand new system that can be costly and disruptive. Another form of AI that can be adopted smoothly into existing systems is that of “Augmented Intelligence”.
AI: Augmented Intelligence
We are continually looking for projects to push the boundaries of using AI. When discussing AI we’re also referring to “Augmented Intelligence” as well as Artificial Intelligence. Augmented intelligence emulates and extends human cognitive function through the pairing of people and machines.
“Forward-looking companies and industry experts agree that augmented intelligence is the most effective way to maximize the value of AI”
Bitjam and AI
At Bitjam we recognise the need for software maintenance, updates and sometimes entirely new systems. We know that the idea of a complete overhaul of your IT-based systems is a concern, yet interoperability might cause long-term challenges in terms of business development. So how can Augmented Intelligence provide the solution?
Bitjam can help you connect with IoT devices to modernise existing services and delay the need for entirely new technology and data systems.
Last year we put our knowledge to the test and created ANNA – a machine learning algorithm that analyses poetry and audibly delivers it in an old-fashioned regional Potteries dialect. Part two of our three part AI Series this Friday will revisit ANNA and share with you the interesting results!
The final part of our three part AI Series next week will focus on the ways you can consider adopting AI for your own systems, how to approach decision-making and the ways in which Bitjam can assist you with the transition.
To learn more or to discuss any projects you have that Bitjam might have the solution to, you can email [email protected]