It is easy to forget that talk around artificial intelligence (AI) began decades ago. In a paper presented at the 3rd international joint conference on artificial intelligence in Stanford, USA (author references below) in August 1973, it was stated: “At the present stage of research in artificial intelligence, machines are still remote from achieving a level of intelligence comparable in complexity to human thought. As computer applications become more sophisticated, however, and thus more influential in human affairs, it becomes increasingly important to understand both the capabilities and limitations of machine Intelligence and its potential impact on society.”
(Authors: Martin A Fischer & Oscar Firschein – Lockheed Research Laboratory; L Stephen Coles & Jay M. Tenenbaum – Stanford Research Institute)
Fast forward to 2017. According to a recent article by R.L. Adams published in Forbes online “…today’s so-called A.I. systems are merely advanced machine learning software with extensive behavioural algorithms that adapt themselves to our likes and dislikes. While extremely useful, these machines aren’t getting smarter in the existential sense, but they are improving their skills and usefulness based on a large dataset.”
Adams cites the following as some of the most popular examples of artificial intelligence that are being used today.
- Siri. Apple’s voice-activated computer that users interact with on a daily basis. She uses machine-learning technology to get smarter and better able to predict and understand our natural-language questions and requests.
- Alexa. The smart home’s hub. Its usefulness and its uncanny ability to decipher speech from anywhere in the room helps users to scour the web for information, shop, schedule appointments, set alarms and a million other things, and also helps power smart homes and be a conduit for those that might have limited mobility.
- Tesla. Tesla cars have predictive capabilities, self-driving features and sheer technological ‘coolness’.
- Cogito. The company is a fusion of machine learning and behavioural science to improve the customer interaction for phone professionals. This applies to millions of voice calls that are occurring on a daily basis.
- Boxever. A company that leans heavily on machine learning to improve the customer’s experience in the travel industry and deliver ‘micro-moments,’ or experiences that delight the customers along the way. It’s through machine learning and the usage of A.I. that the company has dominated the playing field, helping its customers to find new ways to engage their clients in their travel journeys.
- John Paul. A highly-esteemed luxury travel concierge company uses predictive algorithms for existing-client interactions, to understand and know their desires and needs on an acute level. The company powers the concierge services for millions of customers through the world’s largest companies such as VISA, Orange and Air France.
- Amazon.com. Amazon’s transactional A.I. algorithms have evolved allowing it to accurately predict just what we’re interested in purchasing based on our online behaviour.
- Netflix. This provides highly accurate predictive technology based on customers’ reactions to films. It analyses billions of records to suggest films that one might like based on your previous reactions and choices of films.
- Pandora. Based on 400 musical characteristics, each song is first manually analysed by a team of professional musicians based on this criteria, and the system has an incredible track record for recommending songs that would otherwise go unnoticed but that people inherently love.
- Nest. The learning thermostat that was acquired by Google in January 2014 can now be voice-controlled by Alexa, and uses behavioural algorithms to predictively learn from one’s heating and cooling needs, thus anticipating and adjusting the temperature in one’s home or office based on personal needs, and also now includes a suite of other products such as the Nest cameras.
- IBM’s Watson. IBM’s supercomputer combines AI and sophisticated analytical software for optimal performance as a ‘question answering’ machine.
- SAP Leonardo. SAP Leonardo combines adaptive applications, Big Data management, and connectivity in packaged solutions across lines of business and industry use cases, ranging from connected products, assets, and infrastructures to vehicle fleets, markets, and people.
- Microsoft’s Cortana. Cortana is the name for the intelligent personal assistant and knowledge navigator for Windows Phone 8.1 and Windows 10. Cortana builds off Microsoft’s previous voice technology called TellMe, purchased by Microsoft in 2009.
And the future? In Ovum’s report: The impact of deep learning in verticals and Internet of Things, a number of predictions were made concerning the increased adoption of AI.
- Autonomous driving is a key application area for AI/DL (Deep Learning)
- Healthcare is one of the sectors most ripe for disruption by AI
- The telecom industry is also ripe for disruption by AI
- Connected systems will drive AI adoption because big data is generated from sensors and will need intelligent automation to process and make sense of the data
- AI is likely to impact the job market as the debate rages over whether AI introduces a zero-sum game scenario where a job gained by an intelligent machine is a job lost by a human.
Naxian Systems has a secure track record of proactively developing artificial intelligence and learning technologies that provide customers with decreased maintenance costs, enhanced system security and a rapid return on investment. Customised solutions are developed in line with exact customer requirements to ensure best fit and ready adoption of technology.
Naxian will be officially launching its proudly South African and internationally patented AI – Annie – which is an adaptive organic machine intelligence that specialises not in post-event results, but rather in the field of real-time artificial intelligence.