Tag Archives: AI

AI vs EQ

According to The Oxford Dictionary, intelligence is the ability to learn, understand and think in a logical way about things, and the ability to do this well. Emotional intelligence, otherwise known as emotional quotient (EQ) is the ability to manage and understand emotions. I am making a parallel between AI and EQ because I strongly believe there are expectations regarding the level of intelligence machines, robots and autonomous agents are required to have, yet as humans, we have seemingly low expectations of each other to have EQ. Yes, I am comparing EQ to AI, but if AI is a simulation of human intelligence in machines, then this also includes emotional intelligence.

The point I am making is related to my current research which is using an AI tool to investigate its capacity for interactional conversation with humans. I have tried myself to design a tool, and the outcome was a chat interface which had limited capacity to understand the oral input and the output was also very slow and finite. While the potentials for programming a more effective tool are clearly possible from the many examples of virtual assistants now available such as Siri and Alexa, I am questioning if our expectations of their capabilities are perhaps unreasonable. If humans can lack EQ and often not able to empathise with others or communicate effectively, why do we expect intelligent autonomous systems to be able to do this?

We are very far removed from fully understanding the human brain, and until we do, I think we need to be realistic with the potential capabilities of AI.

What does it mean to be human?

With the surge of interest and investment into AI, the question at the forefront of my mind is ‘What does it mean to be human?’ The apparent obsession with AI is to replicate human intelligence on all levels, but the problem I have with this is that I don’t think we fully understand what it means to be human. I think it is impossible to reproduce human ‘intelligence’ without first appreciating the complexities of the human brain. Hawkins (2004) argues that the primary reason we have been unable to successfully build a machine that thinks exactly like a human, is our lack of knowledge about the complex functioning of cerebral activity, and how the human brain is able to process information without thinking.

This is the reason why the work of Hiroshi Ishiguro, the creator of both Erica and Geminoid, interests me so much. The motivation for Ishiguro to create android robots is to better understand humans, in order to build better robots, which can in turn help humans. I met Erica in 2016 and the experience made me realise that we are in fact perhaps pursing goals of human replication that are unnecessary. Besides, which model of human should be used as the blueprint for androids and humanoid robots? Don’t get me wrong, I am fascinated with Ishiguro’s creation of Erica.

My current research focuses on speech dialogue systems and human computer interaction (HCI) for language learning, which I intend to develop so it can be mapped onto an anthropomorphic robot for the same purposes. Research demonstrates, that one of the specific reasons the use of non-human interactive agents are successful in language learning is because they disinhibit learners and therefore promote interaction, especially amongst those with special educational needs.

The attraction is of humanoid robots and androids for me therefore, is not necessary how representative they are of humans, but more about the affordances of the non-human aspects they have, such as being judgemental. In my opinion, we need more Erica’s in the world.

What does 2020 mean for Ed Tech?

A new year AND a new decade, so what does 2020 mean for Ed Tech? Twenty years ago we were getting to grips with communicating via email. Ten years ago iPhones had already been around for three years, but their price bracket pitched them out of reach for the majority of mobile phone users. So here we are in 2020 with driverless car technology being widely tried and tested, and with China witnessing the birth of the third gene-edited baby. So where does this leave language learning and tech, and what is in store for the near future?

Where we are now

Apps, apps, apps… With the 2019 gaming community reaching a population of 2.5 billion globally (statista.com), it is no surprise that apps are an attractive option for learning English. The default options tend to be Babel, Duolingo and Memrise, but there are a plethora of options to choose from. Some recent fun apps I have experimented with are ESLA for pronunciation, TALK for speaking and listening, and EF Hello.

In the classroom however, the digital landscape can be quite different. Low resource contexts and reluctance from teaching professionals to incorporate tech into the learning environment can mean that opportunities for learners to connect with others and seek information are not available. Even is some of the most highly penetrated tech societies 19th century rote based learning and high stakes testing approaches are favoured.

Predictions for the future

Does educational technology have all the answers we need to improve the language output of ESL learners globally? No, probably not. However, society has been so dramatically altered by the impact of technology in almost every facet or our lives, it would be rather odd I feel, to reject it in teaching and learning environments.

In higher education the main concern is data privacy and ethics with exposure to digital areas such as the cloud. Yet, chatbots are starting to become integrated to support students asking university related FAQ’s. Both Differ and Hubert chatbots are being researched for their potential to improve qualitative student interaction and feedback.

Kat’s predictions

In all honesty I think it is a tough call to gauge where we will be with Ed Tech during the next ten years. Data privacy is a considerable issue when incorporating elements of AI into learning fields. This is not an issue with VR and AR and therefore underpins its relevant proliferation in teaching and learning. I feel that VR and AR will continue to mature and provide a more full-bodied learning experience when using VLEs. This may however be a slightly more complex paradigm than some may be able or prepared to employ.

I still firmly believe that reflective practice is a solid foundation for learners using recorded audio or visual content of their language production. So while this doesn’t mean the introduction of a big pioneering tech tool, it highlights its relevance as a reliable learning tool. In the same way, I continue to use Whatsapp, WeChat and Line to share learning content with learners and encourage them to interact with each other, and other learning communities.

Just what do we expect from Chatbots?

Chatbots are the future of conversation intelligence, and can be used to stimulate human conversations. But just what do we expect from chatbots? On the one hand are those that firmly believe intelligent systems will dissipate the element of human interaction in years to come. On the other hand others revel in the delights of giving Siri instructions to challenge her intelligence and gauge the level of response.

Personally I feel that benefits for intelligent systems (chatbots) outweigh the disadvantages, but I am convinced that the advantages will depend on our behaviour and receptiveness to accept their merits. AI cynics were delighted when Microsoft’s Tay was morphed to demonstrate bad behaviour. At last there was proof to substantiate the argument in favour of the severe dangers of AI.

Users of Alexa were slightly disturbed by her random outbursts of laughter, to the extent that her code was re-written to disable a reaction when requested to do so, and to avoid reactions to false positives that try to trick her. This all leads to the question of the levels of humanness we expect from intelligent systems and chatbots, or more to the point the level of humanness we, as ‘humans’, are comfortable with accepting from ‘machines’.

AI: a new currency or the next industrial revolution?

A question that has been on my lips recently is whether AI is set to be the next industrial revolution, or a new currency of the future.

AI Past

The industrial revolution as its name denotes, revolutionised modern industry and manufacturing as we know it today. When the internet emerged in the late 1980s it seemed unimaginable that less than 30 years later, wireless connections and digital devices would have such a pervasive presence in society. New inventions come and go, and technological innovations are created whether they are successful or not, but in most cases they are shaped by the demands of people.

The origins of AI date back to Turing’s computational machine more commonly known as the Turning machine, built in 1935, however the term was coined later in 1955 by McCarthy who defined it as “the science and engineering of making intelligent machines, especially intelligent computer programmes” (ibid 2001:02), in other words trying to understand human intelligence by using computers.

AI Present

During the last 80 years, advances in AI technology have reached astounding levels. It has clearly had a prolific impact on society, to the extent that it has been transformed into a tool in all aspects of life; from banking and email pop ups, to ‘personalised’ selected products, and Siri and Alexa the intelligent personal assistants, and chatbots.

AI Future

Both academic and business investigation and reporting in the field of AI, consider it to be one of the biggest influencers for the future of the market and society. Predicted revenues from AI are unprecedented, resulting in extensive funding and investment from private companies and governments, which highlights the significance of AI in society. China has recently announced they are building a $2.1 billion industrial park for AI research. The past year has witnessed an increasing amount of nations realising the importance of AI in shaping the economics of the future, some even consider it a currency. Bitcoins stand aside, AI is the new currency..

 

 

Reflections 2017

Reflections of 2017: The debate regarding the dangers of spending too many hours glued to an electronic device continue to bubble. The unknown abyss and potential of AI in its many guises continues to be explored. The fears of a robot-controlled world continue to rise. What will 2018 bring?!

Personally, I find all the above extremely exciting. Do I use my phone too much? I know I work too much, and because 80% of my work is online, I am obliged to use a digital device. This has become part of the natural shift in the plethora of work that has become created as geographical boarders are transcended by cyberspace and the power of technology, telecommunications, and IT. Just as technology is shaped by the society that uses it, tech very much shapes society and the way we interact and go about our day-to-day. I view technological developments as portals to opportunities that can be enhanced or were not previously available, especially in a teaching and learning context (whether that be English or dancing to Michael Jackson’s Thriller!!).

I will continue to explore how ed tech can support language learning this year, as I delve deeper into the AI and machine learning chasm. I will also wonder that if smoking hadn’t been banned in pubs and bars, if smartphones wouldn’t be the go to company we choose as we sit alone sipping a coffee contemplating the week, or waiting for a friend.

Learn to dance Thriller with NAO

Speech synthesis, voice recognition and humanoid robots

Speech synthesis or the artificial production of human speech had been around long before daleks on Doctor Who. Apparently, the first speech-generating device was prototyped in the UK in 1960, in the shape of a sip and puff typewriter controller, the POSSUM. Wolfgang von Kempleton preceded all of this with a a speaking machine built in leather and wood that had great significance in the early study of phonetics. Today, text to speech computers and synthesisers are widely used by those with speech impediments to facilitate communication.

Speech to text systems became more prominent thanks to the IBM typewriter Tangora which held a remarkable 20,000-word vocabulary by the mid 1980s. Nowadays speech to text has advanced phenomenally with the Dragon Dictation iOS software being a highly favoured choice. Our world is increasingly becoming dominated by voice automation, from customer service choices by phone to personal assistants like Siri. Voice and speech recognition has been used for identification purposes by banks too since 2014.

I’m curious how these systems work, how they are programmed, what corpus is used and which accents are taken into consideration. Why, because robots fascinate me, and I wonder if it will be possible to “ humanize” digital voices to such an extent that humanoid robots will appear more human than ever because of their voice production and recognition capabilities. It seems like a far cry from the days of speak and spell the kids speech synthesizer of the 80s, but it is looking increasingly more probable as advances in AI develop.

Developments have gone as far as Hiroshi Ishiguro’s Gemonoid HI-1 Android Prototype Humanoid Robot. Hiroshi is a Roboticist at Osaka University Japan, who create a Germaoid robot in 2010 that is a life size replica of himself. He used silicone rubber, pneumatic actuators, powerful electronics, and hair from his own scalp.

Gemonoid is basically a doppelganger droid which is controlled by a motion-capture interface. It can imitate Ishiguro’s body and facial movements, and it can reproduce his voice in sync with his motion and posture. Ishiguro hopes to develop the robot’s human-like presence to such a degree that he could use it to teach classes remotely, lecturing from home  while the Germonoid interacts with his classes at Osaka Univerisity.

You can see a demonstration of Gemonoid here