AI Series Part Two: Bitjam and Anna

1 Jun, 2018

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.


AI Series Part One: AI and Machine Learning

30 May, 2018

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]

Data Encryption

Data Encryption: Why The UK Government Have Got The Wrong Idea

8 Aug, 2017

The UK Government Are Wrong in Reducing the Use of Encryption

Are tech companies doing enough to beat cyber-crime? This question is a hot topic in the tech world right now, as the UK government increases pressure to find effective ways to tackle online communication between hackers and extremists. This blog post is going to look at the explosion of the ‘Internet of Things’, the relevancy of encryption in the healthcare sector and the importance of enabling technology that will help make the people and physical systems of the world, smarter and more efficient.  

The Internet of Things

The ‘Internet of Things’ is the interconnectivity of physical devices such as smartphones, WiFi modems and software, to the internet. IoT is a big revolution for the World Wide Web, due to the wide range of applications and variety of useful software solutions it provides, from anything from smart homes to monitoring radiation levels in nuclear plants. However, due to the nature of such devices, they are prone to hacks that either commandeer the device and program them to do something they’re not intended to do, or they can be controlled to do what they’re meant to but in a devious way.

“When nodes in wireless sensor networks are monitored through internet it becomes a part of Internet of Things. This brings in a lot of concerns related to security, privacy, standardization, power management” ieeexplore.com


What Is Encryption?

End-to-end encryption (E2EE) is a system of communication where only authorised parties  users can read the messages. For example, companies that use E2EE are unable to hand over texts of their customers’ messages to the authorities. A good example of this is the mobile messaging service Telegram. Telegram messages are delivered faster than any other application, are heavily encrypted and can self-destruct.

However, the UK government’s desire to gain more control on encryption would have negative consequences on the tech world as we need this technology to actually develop safer apps and to prevent the compromisation of the IoT. The optimistic outlook of the IoT versus the security threats is a risk worth taking if it enables us to continue to develop solutions to tackle hacking.

Security is the backbone of the internet, which is the reason we need passwords to access our accounts. By enforcing laws on encryption, the UK government would effectively be able to access to your personal information even potentially data from connected devices.

We have to find a balance between national security issues and safety and security of data traffic in healthcare. Healthcare data encryption is used to protect patient confidentiality when information such as medical diagnoses, surgeries and other highly sensitive data is shared between practitioners and other healthcare authorities to provide an effective service to patients.

Many companies building innovative technologies to improve security are using encryption, and as in most areas of IT and computing, innovation in security springs mostly from startup companies, so enforcing encryption laws would also negatively affect small, creative businesses who actually play a pivotal role in successfully discovering, testing, and building out clever new ways to secure cyberspace.

In summary, as much as banking apps need encryption to prevent cybercrime, health apps need encryption to maintain security and privacy. We need to maintain confidence in the sharing of personal data via technologies by further exploring and developing ways to tackle security issues, using the technology of data encryption. Allowing Governments access via backdoors compromises patient confidentiality, and would be damaging to the progress of improving cyberspace privacy.

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