Artificial Intelligence

At Qbit Technologies, we have experimented with some Artificial Intelligence test and applications for some years. Before explaining what is in our opinion the best way to approach AI for your organization, it is better to clarify the different concepts.

• Artificial Intelligence: it is the study and design of intelligent agents able to perform tasks, which require human intelligence, such as visual perception, speech recognition, and decision-making. In order to consider your Artificial Intelligence ready to support your organization, it should pass the Turing test, created with the purpose of evaluate if an Artificial Intelligence can reason, represent knowledge, plan, learn, communicate in natural language and integrate all these skills towards a common goal.
• Machine Learning: it is a sub-aspect of building an Artificial Intelligence. Machine learning is the capacity of automatically learn through experience. It focuses on prediction, based on known properties learned from the training data.
• Data Mining: represents the analysis step of Knowledge Discovery in Databases (KDD) process. Data mining focuses on the discovery of previously unknown properties in the data. The algorithm behind data mining creates association rules in large databases, which then spurred other research on discovering patterns and more efficient mining algorithms. Data mining uses many machine-learning methods. The difference between machine learning and data mining is that in machine learning, performance is usually evaluated with respect to the ability to reproduce known knowledge while in Knowledge Discovery in Databases the key task is the discovery of previously unknown knowledge.

Artificial Intelligence is part of our collective imaginary thanks to cult movies like “A Space Odyssey” by Stanley Kubrick or “Her” by Spike Jonze. Many futurists have speculated on the idea that in the future the Artificial Intelligence could rival or exceed human intelligence. One of those futurists is Ray Kurzweil, who wrote “The Singularity is Near”. In his thesis, he predicts that accelerating progress in technologies will cause a runaway effect wherein artificial intelligence will exceed human intellectual capacity and control, radically changing or even ending civilization in an event called the singularity. The 2045 initiative, a Russian program on Artificial Intelligence is based on that thesis.

Hal 9000 and Dave from “A Space Odyssey”

Regarding the future of Artificial Intelligence and the impact on our society, Stephen Hawking believes that AI has many promising things to offer for future, but not without possible dire consequences. He said:

“success in creating AI would be the biggest event in human history”, and “unfortunately, it might also be the last”.

Anyway, from Apple’s Siri, Google Now Search, IBM’s Watson to driverless cars, Artificial Intelligence is already part of our daily lives.

How can artificial intelligence and machine learning be applied to wearables and the Internet of Things and therefore be part of your organization? There are some great examples, like:

• Lumiata is a medical diagnosis and treatment machine graph based on multi-dimensional probability distribution that contains 160 million data points from textbooks, journal articles, and public data sets to replicate and scale doctor’s knowledge for use by nurses to diagnose and treat illnesses. Add patient-specific data, effects of time and location to Lumiata’s massive data set, the machine learning system is able to generate a clinical model of a patient.
• Google X Nanoparticles help to prevent from diseases, cancers, heart attacks or strokes based on changes to the person’s biochemistry, at the molecular and cellular level. They are released into the bloodstream via a swallowed pill; the patient can use a wearable wristband to view readings of the nanoparticles. Machine learning can be applied to learn to diagnose diseases and changes in biochemistry in the bloodstream through the movement of nanoparticles.

• Atlas Wearables is a fitness band plus intelligence platform, powered by the Motion Genome Project database of movements. The machine learning algorithms can automatically classify your exercise routine in 3D vector, being able to decipher the difference between push-ups and triangle push-ups. The real aim of the start-up is to bring “intelligence into body language and movements”. In future, it can even understand how you are walking, sitting, moving or interacting with others, which can give clues about your mood, physical reaction and energy level.

• BrandEmotions is a perfect tool to quantify consumers’ emotions. It enables brands to measure how consumers feel about their brand experience, from retail, live events, movies, hotels, cruises, amusement parks to advertising. BrandEmotions is a product of Amyx+McKinsey, which displays the emotional reaction of participants to brand engagement, allowing brands to optimize the brand experience, increasing brand loyalty and accurately target products and services at the right time. The machine learning platform measures physiological data captured through a broad range of wearable devices and Internet of Things connected devices to translate data into emotional classifications and intensity.

At Qbit Technologies, we think that the best way to integrate an Artificial Intelligence inside your organization starts from having a reasonable understanding of AI and machine learning concepts. In addition, you should consider Artificial Intelligence has some limitations and none is able to offer a strong AI, today. However, the challenge, for each application sector, is a complete data integration with your company database.

Google’s Alon Halevy, the head of Structured Data Group of Google Research, said:

“no matter how much you speed up the computers or the way you put computers together, the real issues are at the data level.”

In order to try limiting that challenge, we suggest finding strong partners, who have already worked on Artificial Intelligence, like Stanford, Berkeley or Washington Universities.

In conclusion, the best advice we can give you, apart from knowing the topic and finding the right partner, is to start experimenting. Today, you can find open source code and libraries already created by other teams, which can help you to start creating your own Artificial Intelligence, which can fit perfectly with your organization’s core.