Artificial Intelligence

Our expert and leader, Danny Lee has all the skills that your business could need for it's AI needs. Danny practiced over 15 years of tech development managing millions in IT equipment and development.

He has developed multi-national scalable software across Southeast Asia, while managing tech teams around the region. He has built IT software in the areas of big-data analytics and machine learning for intelligence businesses and startups. He has also built scalable SaaS and business workflow solutions.

His team has also built (and highly customised) logistics software, on-demand software, e-commerce software, and booking software for Startups that need stable and reliable development.

Computer scientists have defined artificial intelligence in many different ways, but at its core, AI involves machines that think the way humans think. Of course, it’s very difficult to determine whether or not a machine is “thinking,” so on a practical level, creating artificial intelligence involves creating a computer system that is good at doing the kinds of things humans are good at.

On the other hand, the phrase “machine learning” dates back to the middle of the last century. In 1959, Arthur Samuel defined machine learning as “the ability to learn without being explicitly programmed.” Many web-based companies also use machine learning to power their recommendation engines. For example, when Facebook decides what to show in your newsfeed when Amazon highlights products you might want to purchase and when Netflix suggests movies you might want to watch, all of those recommendations are on based predictions that arise from patterns in their existing data.

Today, all of the largest technology companies are investing in AI projects, and most of us interact with AI software every day whenever we use smartphones, social media, web search engines or e-commerce sites. And one of the types of AI that we interact with most often is machine learning.

Big Data and AI complement each other. In simpler terms, big data is simply useless without AI software to sort and analyse it. Humans can’t do it efficiently because of the quantity. AI can help quantify big data in four possible ways:

1. Detecting Anomalies. AI can analyze big data to detect unusual occurrences in the data. For example, having a network of sensors that have a predefined appropriate range. Anything outside of that range is an anomaly.

2. Predicting Future Outcomes. Using known conditions that have a certain probability of influencing the future outcome, AI can determine the likelihood of that outcome using big data fed into its system.

3. Identification of Trends. AI is capable of noticing any changes or trends in data that are missed by humans. AI can see patterns that humans don’t and also might be able to look for patterns in bars and graphs that might stay undetected by human supervision.

The development of big data technology depends on artificial intelligence, because it uses many artificial intelligence theories and methods. And secondly, the development of artificial intelligence must also rely on big data technology, it requires tons of data for support.

IT professionals and computer scientists quickly realised that the job of sifting through all of that big data, parsing it (converting it into a format more easily understood by a computer), and analysing all of it for purposes of improving business decision-making processes was too much for human minds to tackle. Artificially intelligent algorithms would have to be written to accomplish the enormous task of deriving insight out of chaos.

There are four types of artificial intelligence: reactive machines, limited memory, a theory of mind and self-awareness.

1. Reactive Machines. The most basic types of AI systems are purely reactive and have the ability neither to form memories nor to use past experiences to inform current decisions. This type of AI doesn’t have any concept of the past, nor any memory of what has happened before. It ignored everything except for the present moment. An example of this kind of AI is IBM’s Deep Blue, IBM’s chess-playing supercomputer, which beat international grandmaster Garry Kasparov in the late 1990s, is the perfect example of this type of machine. Deep Blue can identify the pieces on a chessboard and know how each moves. It can make predictions about what moves might be next for it and its opponent. And it can choose the most optimal moves from among the possibilities, therefore cannot predict the future or look into the past history of moves.

These methods do improve the ability of AI systems to play specific games better, but they can’t be easily changed or applied to other situations. These computerized imaginations have no concept of the wider world – meaning they can’t function beyond the specific tasks they’re assigned.

2. Limited Memory. This type of AI contains machines that can look into the past. Self-driving cars do some of this already. For example, they observe other cars’ speed and direction. That can’t be done in a just one moment, but rather requires identifying specific objects and monitoring them over time. With Limited Memory, machine learning architecture becomes a little more complex. Every machine learning model requires limited memory to be created, but the model can get deployed as a reactive machine type.

3. Theory of Mind. This type of AI is yet to evolve and become more complex over time. In this type, AI begins to interact with the thoughts and emotions of humans. Presently, machine learning models do a lot for a person-directed at achieving a task. Current models of this kind have a one-way relationship with AI such as Alexa and Siri bow to every command. 

4. Self-Aware. Finally, in some distant future, perhaps AI achieves nirvana. It becomes self-aware. This kind of AI exists only in the theory, it instils both immense amounts of hope and fear into audiences. A self-aware intelligence beyond the human has an independent intelligence, and likely, people will have to negotiate terms with the entity that is created.