The short answer is that, yes, there is a risk of bias when it comes to artificial intelligence in business. 42% of AI professionals across both the United States and United Kingdom said that they are concerned about AI bias.

This survey was held in the wake of a study that found that 71% of organizations currently rely on AI in order to execute up to 19 different business functions. There are plenty more that use AI to execute up to 50 different business functions.

What Do Businesses Use AI for?

AI being used by organizations is there to execute functions across a number of different departments. This includes finance and accounting, operations, sales, marketing, and human resources. The biggest concerns about bias among IT executives that were surveyed were loss of customer trust and compromised brand reputation.

Some of the biggest challenges in developing unbiased AI algorithms are in determining what data to train AI models with as well as understanding how to input data that relates to AI decisions.

Learning More About AI

One important step in the process is that IT decision-makers are using tools to learn more about why AI makes negative decisions. That includes using tools to check which input data has the biggest effect on an AI decision. Things such as word clouds are used to look at how text input works in association with AI decisions.

To enhance bias prevention efforts, IT experts are planning to invest in more sophisticated white box systems; these are where AI decisions are explainable. Plenty also said that they will hire internal personnel in order to manage AI trust while others will bring on third-party support.

Ultimately, methods are being implemented to combat AI bias but there is still a heavy reliance on AI by businesses across the US and UK. How that will change over the coming years remains to be seen and will likely see even more developments made as the years move forward.

from James F. Kenefick | Entrepreneur https://jamesfkenefick.com/in-business-is-there-a-risk-of-artificial-intelligence-bias/
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As technology progresses, there is a further need for cybersecurity. Cyber criminals are becoming more effective, learning how to bypass current systems with greater ease than ever before. That is where artificial intelligence is beginning to come into play.

Cyber attacks can range from basic phishing and computer viruses to attempts at nation-state advanced persistent threats (APTs) and ransomware. This problem has only been exacerbated by the increased connectivity of Internet of Things (IoT) devices and cloud apps as well as smart devices that make users far more vulnerable to attacks.

Now Is the Time for Change

Because cyber attacks are so common, this is the time to implement AI-enabled platforms to help define foundational defenses. Using AI can be an effective way to stay ahead of threats by using machine learning to help detect and identify malicious activity.

Using machine learning, it might be possible to identify the anomalies that signal threats and help us discover new methods for defeating those threats. AI can also be used to enhance biometric authentication. This can help stop hackers from accessing sensitive company accounts.

Implementing AI Into Protective Practices

Implementing AI within a cybersecurity framework can help to reduce the time spent on both detecting and responding to breaches. This is done by processing massive amounts of data at high speeds to reveal indicators of patterns of malicious behavior and compromise.

Machine learning, meanwhile, has the advantage of a focus on those patterns and behaviors in systems. This includes patterns in network traffic as well as identifying host issues that can cause data breaches and disruptions.

The ability to scan hundreds of millions of vulnerability scans and probes on a daily basis allows companies to keep a step ahead of potentially malicious behavior. This helps avoid those disruptions that can slow business.

We are just beginning to scratch the surface on the capabilities of AI when implemented for cybersecurity measures. The benefits are there already and, with further development, will only continue to grow.

from James F. Kenefick | Entrepreneur https://jamesfkenefick.com/cybersecurity-is-rapidly-expanding-thanks-to-ai/
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There is a belief that with all the data we have available both for people and companies alike, we are losing a bit of the humanity that made companies successful in the past. Eliminating the human mistake element is understandable but we cannot eliminate the human element altogether.

In order to regulate and navigate this new world that we are building for ourselves, we need engineers to create the tools, products, and platforms but we also need philosophers to maintain the human aspect of the work involved.

Deep Learning

Deep learning, in which machines are endowed with thousands of neuron layers that are able to both learn as well as remember, enables machines to reason and to make decisions. Because of this, we are no longer able to assume that we (as humans) are intelligent while machines are not.

Deep learning has changed the believe that only living things can be sentient while investigating, thinking, and understanding. It has also changed the line between categorizing artificial and natural things.

Keeping the Human Element

As explored above, the further we continue to delve into artificial intelligence and machine learning, the blurrier the line between human and machine gets. We have to ask ourselves at what point do machines become too much like us?

There is such a directive towards hard data in order to drive business and to drive learning that the human element may become lost. Adding philosophers to the development of AI can help keep that important line in place while still allowing us to make the developments that can benefit the world.

That battle will continue as progress is made. Keeping that human element while better understanding data is as much the goal as anything else. But this remains a fine line. It is this way because there has to be an acceptance that intelligence is no longer exclusively a human property but something that machines (and animals, to some extent) can have.

Working with this distinction is the key to the development between humanity and machines.

from James F. Kenefick | Entrepreneur https://jamesfkenefick.com/how-tech-companies-could-benefit-from-hiring-philosophers/
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Artificial Intelligence has been in development since the 1950s, believe it or not. The goals have been simple: make intelligent systems that mimic human cognitive abilities. This includes the ability to not only perceive, but understand its surroundings. It includes learning from both training as well as its own experiences and making decisions based on reasoning.

On the flip side, there is machine learning. This is a newly implemented method of using robots and computers in an effort to assist in the educational process. There are a handful of schools that have already experimented with machine learning, testing to see how children react to them as teaching aids.

How Does Machine Learning Relate to AI?

It is generally accepted that successfully understanding human speech, being able to compete at the highest level when it comes to strategic game systems, intelligent routing in content delivery as well as military situations can be technically classified as AI systems.

It is also generally accepted that the narrower the machine learning task, the less like artificial intelligence it is. It is difficult to know at what point a machine learning project is an artificial intelligence effort.

Is Machine Learning AI?

Technically speaking, the answer is yes. Machine learning, which has a subset known as deep learning, is technically a subset of artificial intelligence. The latter is a general umbrella term for any computer program that does something “smart.” 

To expound upon this a bit further, all machine learning is technically artificial intelligence, but not all artificial intelligence is machine learning. An example of this is that symbolic logic – which are rule engines, knowledge graphs, and expert systems – can be classified as artificial intelligence, yet none of them are technically machine learning.

There is still much to know and learn about artificial intelligence and machine learning and how the two truly relate to one another. Until then, we will continue to classify the two as being under the same umbrella, yet completely different.

from James F. Kenefick | Entrepreneur https://jamesfkenefick.com/is-there-a-difference-between-artificial-intelligence-and-machine-learning/
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Data science is one of those terms that feel convoluted until you get down to the bare bones of it. It is essentially the study of data and involves developing new methods of recording, storing, and analyzing data to effectively extract the useful information within that data.

What Does a Data Scientist Do?

Now that we have a better understanding as to what data science is, the question is regarding what data scientists themselves do. This vocation is part of a data science department. This department provides services to other departments within the organization and helps solve complex problems using that data.

Many of the biggest companies in the world are actively hiring data scientists to glean valuable information about the way that they do business. Macys, Micron Technology, and the Walt Disney Company are just a few that are hiring for this relatively new position.

How to Arrive in a Career in Data Science

In essence, data scientists are researchers. They often require advanced training in something like computer science, a different scientific field, or a mathematical field. They also generally require skills such as computational fluency, programming, scientific methodology, advanced math ability, and even soft skills like collaboration and communication.

A strong candidate for a data scientist position that has a combination of all of these skills is known as a unicorn since there is a general shortage of candidates that have all of these skills in their repertoire. There are new development platforms and machine learning technologies that have made it easier for data scientists to accurately perform much of the aspects of computational functions.

Data scientists work largely with machine learning, which ties closely into artificial intelligence. This is the method of using machines to teach. It is still very much a raw and new technology, but the potential is quite high.

Data scientists will continue to be at the forefront of this movement, an integral part in the effort to gather essential data in new ways.

from James F. Kenefick | Entrepreneur https://jamesfkenefick.com/what-is-data-science/
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It’s essential to try to keep improving when you have been placed in a management position. You want to do your best to keep moving up the ladder, and you also want to prove that the faith that was placed in you was right. If you’re going to find true success, then you should endeavor to develop from being a manager to being a true leader. Read on to look at how you can do just that.

Become More Creative

The best leaders are creative and innovative. If you can come up with creative ways to approach problems, then you’re going to be a more effective leader. Managers might be more prone to doing things purely by the book, and they won’t be willing to think outside the box. If you want to develop from a manager to a leader, then you should try to be more flexible in your thinking.

Empathize with Others

Empathizing with others will help you to become a more effective leader. If you don’t have the emotional intelligence that is necessary to relate to other people, then this will be tough. You have to stop looking at employees as numbers, and you need to see them as real people with real problems. Your employees need a leader who understands that life gets tough and who can provide the firm support that they need to keep doing well at their jobs.

It’s also going to be imperative to develop better communication skills if you want to be a leader. A manager might not be as open to listening to employee concerns but a leader will want to take everything into account. Being an excellent communicator is about knowing when to listen and understanding when it’s time to speak up. Work on your communication skills so that you can lead your employees to greater success.

Have the Will to Keep Moving Forward

All managers and leaders will run into setbacks along the way. Tough times might make sure people want to give up, or it might cause them to become stagnant. If you want to be a true leader, then it’s crucial to be persistent enough to keep moving forward. Those who can look adversity in the face and keep working toward their goals will be the leaders who will shape the futures of the companies that they are working for.

from James F. Kenefick | Entrepreneur https://jamesfkenefick.com/how-to-develop-from-a-manager-to-a-leader/
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