Entries by peter lee

Human Learning vs. Machine Learning – What’s the Difference

These days, artificial intelligence is all around us. If you’ve ever used Siri on your iPhone or the live chat feature of a website, you’ve interacted with AI. From a business perspective, the rise of AI can be both exciting and challenging. Furthermore, it’s a concept that isn’t necessarily easy for everyone to grasp. The most common question being asked by individuals who aren’t deeply involved with tech is, “What, exactly, is artificial intelligence?” Perhaps the easiest way to understand AI is to compare it to something that is already widely understood – human intelligence.

How Does Human Intelligence Work?

Generally speaking, human intelligence follows a simple, straightforward and typically predictable pattern. We gather information. We process that information. And we use that processed information to decide what to do next. These three basic steps can be summed up as follows:

Input —> Processing —> Output

Input occurs through sensing and perceiving the things all around us. The senses – eyes, ears, nose, etc. – gather raw input, such as the sight of light or the scent of a flower. The brain then processes that information and uses it to determine what action to take. In the processing stage, knowledge is formed, memories are retrieved, and inferences and decisions are made. Output then occurs in action based on the information processed. For instance, you might hear a siren, see an ambulance in your rear view mirror and subsequently decide to pull over to let it pass.

In order to safely navigate the world in which we live, we must effectively process all of the input we receive. This basic concept is the core of human intelligence, which can further be broken down into three definitive segments:

Knowledge/Memory

People gain knowledge through the ingestion of facts (i.e. the Pilgrims landed in 1620) as well as social norms (i.e. saying “Please” or “Excuse me”). Further, memory allows us to recall information from the past and apply it to present situations.

Inference/Decision

Inferences and decisions are made based on the raw input we receive, combined with our memories and/or built up knowledge. For instance, let’s say you tried a new food a few months ago that turned out to be way too spicy for your taste. The next time you’re offered that food, you politely decline.

Learning

There are a number of ways humans can learn, including observation, example and algorithm. With observation, we determine the outcome on our own. With example, we are told the outcome. Learning by algorithm, on the other hand, allows us to complete a task by following a series of steps. A good example of this would be solving a long division problem.

Human Learning vs. Machine Learninghuman learning vs. machine learning

The main aspects of human intelligence are actually quite similar to artificial intelligence. In the same way that humans gather information, process it and determine an output, machines can do this as well.

Of course, because machines do not have physical senses like people do, the way they gather input differs. For instance, rather than sight or smell, artificial intelligence gathers information through things like speech recognition, visual recognition and other data sources. Think about how a self-driving vehicle can sense obstacles in the roadway or how your Amazon Echo listens and recognizes your voice.

The processing piece of the formula also mimics how human intelligence works. Similar to the way people accrue memories and build knowledge, machines are capable of creating representations of knowledge and databases where information is stored. And, just as people draw inferences and make decisions, machines can predict, optimize and determine what the best ‘next steps’ should be in order to accomplish a particular goal.

Similarly, just as humans learn by either observation, example or algorithm, machines can also be “taught.” For instance, supervised machine learning is akin to learning by example: the computer is provided with a data set containing labels that act as answers. Over time the machine can essentially “learn” to differentiate between those labels to produce the correct outcome.

Unsupervised machine learning is like learning by observation. The computer recognizes and identifies certain patterns and subsequently learns how to distinguish groups and patterns on its own. Lastly, learning by algorithm is the process by which a programmer “instructs” the computer precisely what to do, line by line, using a software program. Ideally, the most effective form of artificial intelligence will utilize a combination of the above learning methods.

The output that results sums up how machines interact with the world around them, whether it’s speech generation, navigation, robotics, etc.

Take, for example, the business use case of cybersecurity threat detection. Artificial intelligence can scan enormous amounts of data and monitor an entire infrastructure in real-time. It can then, through a combination of unsupervised and algorithmic learning, pinpoint anomalies that could potentially represent data breaches. It can then use that information to investigate and test, automatically determining what the next steps should be, whether it’s escalation to a human agent or automatic remediation.

The Future is Now

We have, undoubtedly, only seen the tip of the iceberg as it relates to artificial intelligence and its potential impact on our lives – both personal and professional. As technology continues to evolve and improve at a breakneck speed, AI and machine learning capabilities will also evolve. Why wait? Get ahead of the curve and experience the next generation of automation and AI by taking Ayehu for a test drive today.

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Leveraging Intelligent Automation to Bridge the Skills Gap

Leveraging Intelligent Automation to Bridge the Skills GapWhen it comes to digital transformation, certain distinct skillsets are needed – of which many are in short supply. The area of cybersecurity, for instance, is suffering a remarkable shortage of talent. IT operations that focus on human capital and disparate tools and systems simply won’t be enough to keep up with the staggering pace of innovation.

Modern enterprises must be capable of adapting quickly to the ever-changing and increasingly complex environment while also remaining flexible. Furthermore, a growing number of IT technologies, applications, systems and processes must be adopted and routinely updated in order for organizations to remain competitive.

These demands pose a serious challenge to those enterprises that do not have adequate talent or expertise. For those IT teams that find themselves behind the eight ball, intelligent automation can be their ace in the hole.

The Shift from Human to Machine

Gartner predicts that by 2020, 75% of enterprises will experience visible business disruption due to skills gaps. This is up dramatically from just 20% in 2016. This is a serious concern for business leaders across all industries.

In response, many organizations are already working to add technologies that can augment their existing human resources. In particular, intelligent automation and orchestration is becoming a significant focus. In fact, Gartner lists AI and machine learning strategy development/investment among “the top five CIO priorities.”

Making the shift from human to machine results in two distinct advantages. One, because intelligent automation is capable of performing massive amounts of error-free work, productivity skyrockets. Second, with the addition of intelligent automation, existing human workers can apply their advanced skills to more important business initiatives, such as growth and innovation. And thanks to machine learning and AI technologies, decision-makers can avail themselves of data-driven support.

A Match Made in IT Heaven

With intelligent automation, organizations facing the challenge of budgetary restraints can build highly functioning, agile IT operations without the need to hire additional staff. Existing personnel can be trained and reskilled to become versatilists — those who can hold multiple roles, most of which will be business, rather than technology, related.

The key to delivering digital value at scale is having the right people talent,” says Terrence Cosgrove, research vice president at Gartner. “Currently there just isn’t enough talent with the digital dexterity for hire, so I&O leaders will need to develop this core competency in the talent they already have.

With the help of intelligent automation, IT departments can operate at maximum efficiency, saving time and money in the process. In fact, this technology has the potential to position forward-thinking enterprises at the forefront of digital transformation, despite the growing talent shortage.

Could your organization benefit from this “ace in the hole?” Find out today by taking Ayehu for a test drive.

 

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