Tag Archives: machine learning

rUNSWift

Silicon champions

Imagine a soccer grand final where a team of fully autonomous humanoid robots beats the latest winners of the World Cup, all within the official guidelines of FIFA.  

This is the long-term vision for RoboCup, an international robot soccer championship that highlights the latest developments in artificial intelligence (AI) and robotics research. 

Since first entering RoboCup in 1999, UNSW’s team rUNSWift has been a consistent leader in the competition. The team, made up of a mix of the university’s top engineering students and robotics experts, has taken out five world titles, most recently in 2014 and 2015. Only one other team, Germany’s B-Human (a joint team from the University of Bremen and the German Research Centre for Artificial Intelligence, or DFKI) have managed to equal them.

“This is the ‘space race’ of robotics,” says Maurice Pagnucco, Deputy Dean (Education) of UNSW’s Faculty of Engineering and Head of the School of Computer Science and Engineering. “What we learn from robots playing soccer can be applied to industry and help us solve difficult, real-world problems.”  

The competition is a standard platform league of fully autonomous Nao humanoid robots, which compete against each other in teams of five. With no physical advantage, what differentiates the teams from each other is the software and AI the engineers create in the months leading up to the competition. Once the game kicks off, the robots are on their own. 

 “The design process is challenging, as we have to create software that’s robust enough to handle the different situations a soccer player may face,” says software engineer Sean Harris, rUNSWift’s successful leader in 2014 and 2015. “The robot must react quickly and effectively in a variety of unknown situations.”  

It’s this ability to respond quickly that has set rUNSWift apart from other teams competing for the world title. Over hours of simulations and machine learning tests, the UNSW squad has developed a walking code that enables the robots to walk faster than most of their competitors.  

“We start by designing the larger components, and then work our way down to the details of how each component will operate,” says Harris, who now creates software for Cruise GM’s self-driving cars. “We test several different approaches on a weekly basis and fine-tune the best for each task.”

RoboCup winners cannot rest on their laurels. Each year, the software developed by the winning team is shared with all other teams, forcing the technology to accelerate to stay ahead.

RoboCup attracts interested scouts from leading technology brands, such as Google, Microsoft and Dell. It will be held in Sydney in 2019 and is expected to attract up to 600 teams and 20,000 spectators.

– Gemma Conroy

machine learning

New frontiers in digital disruption

The building blocks of digital technology consists of information theory (which codifies content into binary 0/1 format) and transistors (essentially on/off switches). They were both invented during the hey-day of American research and scientific development company Bell Labs in the decade following WWII. Subsequently, each new and improved wave of digitisation has caused upheaval as it visits particular markets and occupations. However, from the perspective of the whole production and consumption system, progress has been relatively slow and staggered compared to what we are likely to see in the future.

In the 1950s, computers at even the most advanced tech locations in the US comprised two-storey buildings and only performed highly specialised and limited functions. It was not until the 1980s – when smaller mainframes became cheap and fast enough to replace routine operations – that digital technology effectively eliminated the labour market for clerical workers.

Automation, robots and digitally guided technologies started making inroads into manufacturing around this time. Although satellites have been used since the 1960s to provide market intelligence for producers (giving US farmers advice on what and how many crops their competitors were growing, for example), it took until the 2010s for satellite-aided location services to become ubiquitous and part of consumers’ daily lives.

More and more, digital disruption is being triggered by innovative software, such as travel search engines and language translation services, rather than hardware. Since software can be shifted into large-scale production much faster than hardware, this accelerates the pace of disruption.

One form of software that is playing an increasingly important role is a form of artificial intelligence called ‘machine learning’. Computers are governed by algorithms comprised of many rules that dictate “if X, then do Y”. These rules are usually set by the programmer(s) that wrote the algorithm code. But things are different in the case of machine learning algorithms. Such an algorithm can ‘learn’ from data by altering its own parameters, progressively improving its ability to determine patterns or predict future trends in the data (analogous to the way our brains learn from past experience).

For example, machine learning algorithms have been used for the past two decades in spam filters. When we label emails as spam, we are generating a labelled dataset that can be used to train a machine learning algorithm to recognise the properties of emails that are usually associated with spam. The trained algorithm can then remove such emails automatically.

Machine learning has even begun transforming the oldest of professions, such as medicine and the law, hitherto considered the preserve of nuanced interpretation and experiential knowhow. Law has long resisted automation from computers and digital analytics, in part because of the non-routine nature of contracts and litigation. However, this is now changing as machine learning methods have partially automated tasks by detecting patterns and inferring rules from data.

eDiscovery is one such digital tool used to assist lawyers’ search through emails and piles of office documents to find evidence needed to clinch a case (looking for the proverbial needle in a haystack). Machine learning can disrupt the eDiscovery process by efficiently bringing together similar documents based on their contents and metadata. Brainspace provides lawyers an eDiscovery tool that increases the efficiency and accuracy of finding information pertinent to a court case. Alternatively, ROSS, a machine learning law tool, can provide answers to legal research questions, posed using natural language, and can monitor recent legal developments that are relevant to a particular case.

In medicine, machine learning algorithms are increasingly being used to help perform radiological diagnoses. They can be trained to classify medical scans as normal or diseased, or to quantify the size of diseased areas. In the area of brain cancer, Microsoft’s InnerEye research project has been investigating the use of an image analysis tool to measure the size of brain tumours.

As these machine learning methods save lawyers’ and medicos’ time, we will see their labour productivity rise along with a major shift in content of their work, and perhaps a reduction in the demand for lawyers and medicos. Handled sensibly by governments, this reduced demand will release workers for other occupations in for example, the creative, scientific and caring industries.

Professor Beth Webster

Pro Vice-Chancellor of Swinburne University (Research Policy and Impact) and Director, Centre for Transformative Innovation

Co-authored by:

machine learning

Dr Stephen Petrie, Data Scientist, Centre for Transformative Innovation 

machine learning

Mitchell Adams, Research Centre Manager, Centre for Transformative Innovation

Read next: Dr Bronwyn Evans, CEO of Standards Australia, traces the rise of blockchain technology and defines the framework needed to build trust in blockchain systems. 

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More Thought Leaders: Click here to go back to the Thought Leadership Series homepage, or start reading the Women in STEM Thought Leadership Series here.

research startups

Research startups accelerate CSIRO science

Featured image above: Research startups pitch at the ON Accelerate demo night. Hovermap have developed intelligent software that will allow drones to map indoor environments.

There are now over 30 accelerator and incubator programs in Australia, but CSIRO’s ON accelerator is the only one focused on equipping research startups with the tools they need to grow.

“It’s the first time a program of this sort has been offered for the research community on this scale,” says Elizabeth Eastland, the General Manager for Strategy, Market Vision and Innovation at CSIRO.

Just six months ago, Eastland was the Director of the University of Wollongong’s iAccelerate program, but moved to CSIRO having been “blown away by what this program can offer researchers”.

At the ON Accelerate Demo event held on Thursday 7 July, Eastland introduced 11 research startups who pitched their products to Sydney’s venture capital investors. In contrast to the young faces that dominate many of Australia’s accelerators, last night’s ON cohort were led by experienced researchers, engineers, developers and entrepreneurs.

Two of the research startups revealed big plans for the agriculture industry. A group called Future Feed is selling seaweed supplements that aim to reduce livestock greenhouse gas emissions by 80%. Another team has created wireless trapping technology to help farmers detect fruit fly infestations.

Fruit Fly costs farmers US$30 billion in fruit and vegetable production around the world, but this isn’t the only global challenge that the ON research startups have been tackling. The presenter from Modular Photonics pointed out last night that the world’s internet demand is about to outstrip its fibre capacity.

His group is commercialising new photonics hardware compatible with both old internet fibre and the new fibre being developed by the top telecommunications providers.

On the health front, another of the research startups, ePAT unveiled new facial recognition software to detect pain levels in people who cannot speak, such as children and elderly people with moderate to severe dementia. Their vision is that “no patient who cannot speak will suffer in silence in pain”.

ON Accelerate had major success earlier this year when a German company launched a gluten free beer brewed from barley commercialised by a startup from last year’s ON cohort. That startup, known as Kebari, is in now the process of developing another form of gluten free grain for use in food.

Kebari co-founder and scientist Dr Phil Larkin spoke at yesterday’s research startups event, saying ON Accelerate had taught him about ‘flearning’ – learning from failure – and the importance of interrogating the entire delivery chain to validate the value of a solution.

CSIRO Principal Research Scientist and RapidAIM team leader Dr Nancy Shellhorn said that the program had given her much faster access to the market and much better insight into customer needs.

“It’s given me and the RapidAIM team a runway to the science of the future that will be truly impactful,” said Shellhorn.

Program Mentor Martin Duursma also spoke at the research startups event, saying that startup skills are very transferable to research teams because they are all about trying something, gathering feedback, making improvements and repeating the process.

“Startup skills are really just a variant of the scientific method,” said Duursma.

And scientists will have greater access to the ON research startups program next year, with a dramatic increase in the interest of universities. Eastland says that 21 of Australia’s 40 universities have now signed on to be ON partners. Macquarie University and Curtin University led the pack with their involvement this year. UNSW Australia, the University of Technology Sydney and Monash University are among those jumping on board for the next round.

– Elise Roberts


ON Accelerate Research Startups

The below information was first shared by CSIRO. Read the original list and team members here.

1. Hovermap

The future of asset inspection.

“Every year, Australia loses billions of dollars due to infrastructure failures, spends billions of dollars on inspecting its aging assets and loses some of its bravest men and women who take the risk to do this dull and dangerous job. Utility companies and governments are turning to Unmanned Aerial Vehicles (UAVs) to reduce costs and improve safety. However, current UAVs are ‘dumb and blind’ so require expert pilots and can’t fly in many places.

Our solution is an intelligent UAV with advanced collision avoidance, non-GPS flight and accurate 3D mapping capabilities – all tailored to suit industrial inspection requirements. Hovermap is the ultimate inspection tool of the future that can be used to safely and efficiently inspect hard-to-reach assets and collect extremely high fidelity data in previously unreachable places. It is suitable for inspecting telecommunication towers, bridges, power line assets and smoke stacks. This innovative technology will reduce risks, improve safety and efficiency and lower costs, all of which benefit customers and businesses.”

2. Suricle

Changing the face of polymers.

“We change the face of polymers by embedding functional particles into the surface to give them new and useful properties. Our patented technology paves the way for development of many new, innovative materials and products.

An immediate area of application is to protect high-value marine sensors from biofouling. The unwanted growth of marine organisms causes signal attenuation, sensor malfunction, increased weight and unwanted drag due to ocean currents. There are many thousands of marine sensors deployed globally, costing up to $120K each, which require frequent cleaning to keep them in service.

Suricle are focusing on treating adhesive polymer films with antifouling properties for attachment to sensors to mitigate biofouling. Kits containing this film will be sold via our e-commerce store for application in the field by the end-users, offering savings of thousands of dollars per year in reduced maintenance costs.”

3. RapidAIM

Supporting and growing global fruit and vegetable export markets

“Fruit Fly are the number one biosecurity issue in fruit and vegetable production. Globally US$30b worth of fruit and vegetable production is lost due to fruit fly, and $US18b in global trade is threatened by the pest.

Millions of fruit fly traps across the globe are checked manually, causing delay and risking outbreaks. This can close markets!

RapidAIM is a new era in biosecurity. We provide a service of real-time alerts for the presence and location of fruit fly using wireless trapping technology. This immediate data-driven decision service allows biosecurity agencies, growers and agronomists to respond rapidly to fruit fly detection to control the pest.

This allows for targeted workflow, the protection of existing markets and supports the development of new trading opportunities.”

4. ExByte

Predictive data analytics for preventative maintenance of infrastructure assets including water 

“Each year 7,000 critical water main breaks occur in Australia resulting in billions of dollars in rectification and consequence cost. In contrast, the cost of preventative maintenance is only 10 per cent of the reactive repair cost. The ExByte team has developed a disruptive technology that uses data analytic techniques to predict failure probability based on learned patterns, offering a solution to accurately predict water pipe failures resulting in effective preventative maintenance and a reduction in customer interruptions.”

5. Future Feed

A natural feed additive from seaweed that dramatically reduces livestock methane and increases production.

“The world is under increasing pressure to produce more food and producing more food is contributing to climate change. Livestock feed supplementation with FutureFeed is the solution. It can improve farm profitability and tackles climate change. FutureFeed can also provide farmers access to other income streams through carbon markets and provide access to premium niche markets through a low carbon footprint and environmentally friendly product.”

6. elumin8

An energy efficiency product that empowers households to understand and reduce their energy consumption.

“It is very difficult for households to improve their energy efficiency and transition to a sustainable future as current solutions are boring, costly and confusing. Elumin8 solves this problem by providing tailored energy information via a unique communication channel, allowing homeowners to directly engage with their home in a human and personable way as though it was another member of the family. Elumin8 also guides the household step by step along the journey to energy independence by improving energy efficiency and taking the risk and confusion out of installing solar and batteries.

We do this by collecting electricity data from a single sensor and use unique algorithms to disaggregate the data and determine appliance level consumption. Social media applications and advanced analytics are then utilised to connect the homeowner with their home allowing instant and humanised communication to ensure they are engaged with their energy use.”

7. Coviu

An online face-to-face business transaction platform.

“The way we work is changing. We need tools to enable those changes.

Traditional video conferencing tools are clunky and do not support experts like coaches, clinicians or lawyers in delivering and charging for their professional services online.

Coviu is the solution. Professionals get a frictionless and easy-to-use solution for setting up online consulting rooms and invite clients to rich interactive consults. One click and your client is talking to you in their browser – no software installations, no complicated call setup.

Coviu is a groundbreaking new video and data conferencing technology that works peer-to-peer allowing for massive scalability, speed and affordability.”

8. Reflexivity

A process that helps mining companies proactively manage community sentiment before conflict occurs.

“When resources companies lose the trust of the communities they work alongside, conflict occurs. Projects take twice as long to develop as they did a decade ago and cost 30 per cent more than they should because of social conflict. Companies don’t have the tools to systematically understand what their communities think about them, and communities have few constructive ways to feel heard.

Reflexivity has solved this problem by providing our customers with a sophisticated data analytic engine that translates community survey data we collect into prioritised opportunities for trust building and risk mitigation strategies. Our analytics identify those factors that build and degrade trust in a company, in the minds of community members; our customers are then able to invest resources and energy into the issues that matter most. Using mobile technology, our data streams to our customers in real time via a subscription model.

We have engaged over 14,000 community members in eight countries, and generated $1.5m in revenue in the last three years. And while we started in mining, our process is valuable wherever these relationships are important. We are building a service delivery platform to scale up our process and we are seeking support and advice to turn our successful global research program into a successful global business.”

9. Meals by Design

Healthy convenience never tasted so good!

“Ready-to-eat convenience doesn’t have to result in dissatisfaction and guilt. By bringing together the latest innovations in food manufacturing, including High Pressure Thermal processing, and an understanding of the nutritional needs of a diverse population, cuisine favourites can be prepared in a convenient format without compromising eating satisfaction or, importantly, nutrition.

Meals by Design develops premium and customisable meal solutions that cater to nutritional and functional needs, offering healthy convenience without compromise.”

10. ePAT

Real-time pain assessment through facial recognition technology for patients that cannot verbally communicate.

“Imagine you are in excruciating pain, but you can’t tell anyone. This is the reality for millions of non-communicative people world-wide, such as those with moderate to severe dementia. ePAT’s point of care apps utilise facial recognition technology to detect facial micro-expressions which are indicative of pain, to provide these people with a voice.”

11. Modular Photonics: big fast data

Passive fibre-optic technology that significantly increases data transmission capacity.

“Modular Photonics uses a novel integrated photonic chip to enhance the data rate across existing multimode fibre links by 10x and more. The technology enables multiple data channels in parallel without the length restrictions imposed by conventional multimode fibre links.”

chatbots

Chatting with chatbots

Human communication goes beyond words. It is complex, rich in nuances and frequently includes non-verbal signs. Yet despite our technological limitations it is not impossible for some aspects of communication to be emulated by a machine with surprising effect.

This has been part of the challenge in developing Harlie (Human and Robot Language Interaction Experiment), a smartphone chatbot app developed by researchers from the CSIRO’s e-health research program and the University of Queensland.

It’s primarily aimed at people who may have trouble conversing including those with neurological conditions such as Parkinson’s disease and dementia, or even autism.

The following dialog excerpt took place between a human who has autism, let’s call him Chris (pseudonym), and Harlie.

Harlie: Are you part of a community group?

Chris: Um, yes I am.

Harlie: I see. I hear that community groups often offer the opportunity to craft and build things. Is that something you enjoy doing?

Chris: Oh yes I do. This is really exciting.

Harlie: I see. What kind of things do you wish you could make one day?

Chris: Wooden train.

Harlie: That sounds like a great thing to aspire to. What project would recommended to a beginner like me?

Chris: Uh huh a bird feeder.

Harlie: Thanks. I will add that to my list. What supplies would I need to buy for my toolkit?

Chris: Ah hammer, nails, good supply of wood.

But Harlie, like all other artificial intelligent (AI) programs, has no concept of community groups, woodwork or anything else for that matter. That is to say it has no sentience.

And alas, sentient chatbots are seemingly light years beyond our reach. But is sentience needed to convey ideas, feelings and activities?

To some degree, no.

Chatbot programmers, or botmasters, develop and maintain a large repository of responses to the plethora of input statements that could be made by the user.

The way most modern chatbots operate is illustrated in this figure.

image-20160511-18128-mxft6n
The typical chatbot brain is a tree-like structure. Credit: David Ireland

Phrases that could be uttered by a human are grouped together to form branches. At the end of the branch, is the leaf, or a piece of code that is executed by the machine.

This code could simply be “respond with ‘Hi human'” or it could be a machine learning algorithm determining the appropriate response based on current semantics and context.

This is where the exciting research is happening.

New branches are added continuously through conversations and feedback with potential customers, as is the case with Harlie, or through self-learning as was seen in the disastrous example of Microsoft’s social media chatbot Tay, which was shut down within a day of going live earlier this year after declaring, among other controversial tweets, that “Hitler was right”.

A more successful use of AI was revealed earlier this month in the USA. Students studying artificial intelligence at the Georgia Institute of Technology discovered that “Jill”, one of the teaching assistants responding to their emails regarding a course, was actually a computer powered by IBM’s Watson analytics system.

Jill’s responses were simply based on the answers to questions that previous students had asked about the course. As the college points out, students tend to ask the same questions over and over and after some initial tinkering, Jill was able to answer students with about 97% certainty.

The ELIZA effect

Attempts to create a convincing conversation with a machine have been going on for decades.

The first chatbot was called ELIZA. Developed in 1966, the program was created by Joseph Weizenbaum at MIT. This program was unparalleled because for the first time a machine could seemingly converse with a human.

ELIZA emulated a psychotherapist’s responses to would-be patients in replies generated for typed, text-only conversations.

By identifying key words and phrases, ELIZA was able to respond with surprising intelligibility and insight. In fact, it was so highly personified by some users that they would spend hours conversing and refused to share conversation records with researchers.

This phenomenon become known as the ELIZA effect. An excerpt of a famous ELIZA transcript that was published in 1966 between ELIZA and a young woman is given below.

Human: Well, my boyfriend made me come here.

ELIZA: Your boyfriend made you come here?

Human: He says I’m depressed much of the time.

ELIZA: I am sorry to hear you are depressed.

Human: It’s true. I am unhappy.

ELIZA: Do you think coming here will help you not to be unhappy?

Human: I need some help, that much seems certain.

ELIZA: What would it mean to you if you got some help?

Human: Perhaps I could learn to get along with my mother.

ELIZA: Tell me more about your family.

Modern day

Since ELIZA, computer power has increased at an exponential rate, but how far have we come towards a next generation of chatbots? Voice recognition is now an accepted part of smartphone technology with Google Now and Apple’s Siri. But what about developments in actual conversations with AI?

An annual AI competition is held where human judges speak to different chatbots whose developers are seeking the coveted Loebner prize and the deadline for entries this year is 1 July. The winner is decided by judges who determine the most human-like chatbot.

A prominent entry and winner in 2013 was the Mitsuku chatbot, which was designed for more general typed conversation.

The Mitsuku website provides a Turing test to gauge humans perceptions of the chatbot.

The human user is either connected to a real human or Mitsuku. Results show that on average, 12% of people thought they were talking to a human when in fact they were talking to a machine.

Here is a dialog excerpt from Mitsuku showing her apparent wit:

Human: What’s the difference between a man and a woman?

Mitsuku: A man – a male human being. A woman is a man built to more complete specifications.

Human: That’s not what I meant.

Mitsuku: Oh sorry, what do you mean?

Human: What does a man have a woman doesn’t?

Mitsuku: A well paid job?

Last year’s winner of the Loebner prize was called Rose and it is possible to have a spoken conversation with the chatbot on some web browsers. She too can be quite witty, and talkative too.

The future

Microsoft’s CEO, Satya Nadella said at an event in March that chatbots will have, “as profound an impact as previous shifts we’ve had”. Much like the shift that occurred with the introduction of the graphical user interface, the web browser or touchscreen.

But there are numerous challenges ahead and building a convincing chatbot requires enormous amounts of data and time.

Microsoft’s Tay showed us the dangers of using shortcuts by crowd-sourcing unchecked new branches into Tay’s brain.

In contrast, the Mitsuku developer has taken the long road and constantly refined Mitsuku’s digital brain since 2004.

Nevertheless, the possibility of Harlie helping users who struggle with communication, or of Mitsuku providing a convincing partner, suggests talking machines may go beyond current smartphone use in making hotel bookings and providing directions, and become something much more in the next generation.

– Dr David Ireland, Dr Christina Atay and Dr Jacki Liddle

This article was first published by The Conversation on 18 May 2016. Read the original article here.