AI: the rise of the bots
AI and machine learning must underpin the digital transformation strategies of all organisations including, says Civica's Tim Franklin, the government
At a time when the majority of our communications are online and consumers are increasingly expecting information at their fingertips, investing in automation technologies to support always-on communication is crucial.
From our tailored Spotify music recommendations to our interactions with Alexa, more parts of our daily routine are becoming automated, and we expect the same levels of digital engagement across all aspects of our lives.
As highlighted in our recent report, Better, faster and more innovative public services, 40% of millennials confirmed they use chatbots on a daily basis.
With intelligent bots entering the market, AI presents an immense opportunity for organisations to change the way we work, appeal to a wider audience and boost productivity by up to 30%.
Drawing on over 25 years’ experience across public services, our North Star innovation lab has identified that one area where AI will have a significant impact is in the public sector, which is still in the early stages of its AI maturity curve.
This is where conversational AI, self-service chatbots and messaging apps come in.
From improving access to vital information at a time of crisis to sharing guidance from health services, schools or local authorities, digital citizen engagement via chatbots can help automate many of the repetitive requests and processes that are adding additional pressure to an already stretched public sector workforce.
Choosing your bot
Local and central government leaders share a common goal to improve the speed, quality and efficiency of public services.
Yet with so many automation solutions on the market, it’s important that IT leaders outline the desired outcome, measure projected ROI and are realistic about their constraints, before launching into any implementation.
There’s no one size fits all approach.
Organisations would be wise to spend time researching the different bot solutions available, as well as interviewing stakeholders across the business to determine their needs, budget and the overall level of IT skills and experience.
When overhauling legacy IT systems, organisations should start small and scale up. A basic chatbot solution is a good building block to start accelerating your AI journey and there are three levels of application to choose from:
- FAQ bots are programmed to answer a set list of questions, which means they can reduce the amount of time employees spend responding to repetitive citizen queries
- Task-focussed bots support citizens through a process, for example booking an appointment
- Voice-enabled bots make self-service more accessible to an older demographic who may not know how to use or feel comfortable with online messaging services
Understanding your end-users
Organisations should consider how services will be used by different groups of people.
For example, how accessible do they need to be? What is the demographic split of the future users and will voice be important to them? User research groups can help organisations create ‘personas’ for their future users, testing different technologies on these personas to ensure they’re meeting the needs of a diverse set of users.
Putting the user at the heart of the design phase of an AI implementation and taking a ‘citizen-as-consumer’ approach will ultimately ensure that the technology meets the needs and expectations of citizens.
Bots for all
Regardless of the scale of your operation, efficient and streamlined communications with citizens should be top of the agenda.
In today’s on-demand world, we have all come to expect the same service from the government as we do from our bank or favourite online retailer.
Enabling AI-powered services is the first step an organisation can take to close the gap between current citizen expectations and reality, as well as to streamline the distribution of vital information and manage requests when demand spikes.
Google is using AI to design faster and improved processors
Engineers at Google are now using artificial intelligence (AI) to design faster and more efficient processors, and then using its chip designs to develop the next generation of specialised computers that run the same type of AI algorithms.
Google designs its own computer chips rather than buying commercial products, this allows the company to optimise the chips to run its own software, but the process is time-consuming and expensive, usually taking two to three years to develop.
Floorplanning, a stage of chip design, involves taking the finalised circuit diagram of a new chip and arranging the components into an efficient layout for manufacturing. Although the functional design of the chip is complete at this point, the layout can have a huge impact on speed and power consumption.
Previously floorplanning has been a highly manual and time-consuming task, says Anna Goldie at Google. Teams would split larger chips into blocks and work on parts in parallel, fiddling around to find small refinements, she says.
Fast chip design
They have created a convolutional neural network system that performs the macro block placement by itself within hours to achieve an optimal layout; the standard cells are automatically placed in the gaps by other software. This ML system should be able to produce an ideal floorplan far faster than humans at the controls. The neural network gradually improves its placement skills as it gains experience, according to the AI scientists.
In their paper, the Googlers said their neural network is "capable of generalising across chips — meaning that it can learn from experience to become both better and faster at placing new chips — allowing chip designers to be assisted by artificial agents with more experience than any human could ever gain."
Generating a floorplan can take less than a second using a pre-trained neural net, and with up to a few hours of fine-tuning the network, the software can match or beat a human at floorplan design, according to the paper, depending on which metric you use.
"Our method was used to design the next generation of Google’s artificial-intelligence accelerators, and has the potential to save thousands of hours of human effort for each new generation," the Googlers wrote. "Finally, we believe that more powerful AI-designed hardware will fuel advances in AI, creating a symbiotic relationship between the two fields.