The impact of Artificial Intelligence on business operations is said to be vast. So, how do you navigate this expanding area of technology and its impact on your business?
Our 360-degree, 24-hour connectivity means we are all part of the “smart” Artificial Intelligence revolution.
It is no longer exclusive to mathematical super-smart statisticians or coders in Silicon Valley. Be it by way of collaborative filtering on Amazon; pinning potential interested books to your profile, or, Google employing ranking techniques to list the most relevant search outcomes to layers of data clusters.
Individually and collectively, we are adding to the continual improvement (learning) of algorithmic processes and systems on a daily basis, i.e. contributing to Artificial Intelligence.
AI isn’t new, but it’s a growing force to be reckoned with
It is easy to think of AI as a new concept, but it has been around for a long time. In fact, the idea was birthed by computer scientists at the Dartmouth Conferences in the USA in 1956. The expectation was that computers will be able to think, reason, and use its senses at a capacity equal to or greater than humans inall scenarios.
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Since conception, the development of Artificial Intelligence might not have reached the heights its founders imagined, but great strides have been made. Systems have become highly sophisticated, and we have trained computers to complete narrowly-defined tasks better than humans.
Today, smart systems can sense, assess and act on large amounts of data instantly. This step-change in AI’s recent progress is due to many progressive factors, but the three key factors include:
1. Computational power
Computers are faster and better than ever before; accelerating data consolidation, inferences and/or decision planning.
2. Data volumes
Data is tracked, collected and monitored exponentially, improving pattern recognition capabilities, expanding sample sizes and stress-testing historic statistics.
3. Reasoning applications
We are teaching machines alternative processes, paired with multiple rules within increasingly complex ecosystems.
Of course, different systems will use different degrees of complexity of the above factors to create inferences needed given the ultimate objectives. But, in general we can agree that systems that recommend options based on your past behaviour, or can recognise a pattern in your behaviour, images you have liked, are pretty smart.
AI systems are delivering services considered intelligent and creative, with few companies able to survive, save costs or smartly compete without the use of artificial intelligence and machine learning.
Machine Learning (an application of AI) in which programmes analyse data, learn from it, and make informed decisions. The result, more sophisticated systems.
Enter Deep Learning
As a subset of Machine Learning, Deep Learning makes it possible for computers to make a decision/determination using its own logic mechanism called an artificial neural network.
Similar to the human brain, an artificial neural network is a structure with layers of algorithms which, within each layer, contains neurons that parse and share data amongst each other until a final determination is made.
It is important to consider the huge amounts of data needed by these neural networks to fine-tune itself to become almost perfect in its function. As a reference point, in 2012 Andrew Ng at Google used images from ten million videos to train his deep learning system to identify images of a cat.
As the world becomes more complex, we need to remain dynamic by blending our human resources with high-quality computer and data science. This blend equates to intelligence, a unique attribute most humans guard dearly.
Take Siri (Apple’s iOS voice command software) for example, whether we call her/him/it an output of cognitive computing, an intelligent assistant, or a predictive analyst – these are all different aspects of AI.
Simply put, we consider these capabilities of our hand-held guider intelligent and, often command it as we would a human.
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So, what does all this mean for businesses?
Within the transport and logistics industry, significant opportunities exist for AI development and implementation to reduce costs, share capacity, optimize routes and grow revenues. We use machine learning and smart matching algorithms to create a marketplace where traders, shippers, agents and transport carriers can connect, match and bid on volumes.
We also leverage commodity volumes, production trends and trading volumes to match these with underutilised assets (or space) on vehicles already on voyages. By finding matches on routes, the system registers success rules, enabling predictive future route and commodity probabilities. We power a new way of collaborative thinking across enterprises for faster logistics, better economics and a significantly lighter carbon footprint.
Leading logistical firms around the world already employ big-data analysis to optimise and streamline their operations. However, the capital and time required to analyse all this data (sufficiently) remains a major burden for many small to medium sized companies. Attaining large data sets, affording capital and labour costs required to label and categorise these data sets, paired with inherent bias (given internal sampling) remain a burden.
Addressing this - ideally companies within the sector will recognise the power of an independent data aggregator that can consolidate, transfer and infer outcomes that can support collective and individual dynamic strategic responses.