Tag: AI

Jared Molko addresses the concern around AI and its impending impact and disruption of jobs.

He notes that anything that is repeatable can be automated, to illustrate the context of how AI will change employment.

He said that the longer-term impact is unknown, but that in the near-term, employment will likely be man working alongside machines for increased productivity.

Soft skills like empathy, communication and active listening will become even more important as people re-asses value.

Molko emphasizes that we will need to be perpetual learners in this new world.

We will need to develop resilience and responsiveness to manage the changes.

Having a specialist mindset has worked well when times are certain. But we now live in times of uncertainty and change.

What skills and knowledge is valuable for the 21st century?

We should be learning general, human skills, which are transferable to any type of job, particularly the 4 C’s:

• Critical thinking
• Collaboration
• Communication
• Creativity

We need to be generalists and specialists. But we’ve focused on specialization in the industrial age.

What is needed in this new age will be resilience, flexibility and the ability to transfer skills, perhaps many times.

In brief, to adapt to an AI world, we need to become more adaptable.

Jared Molko is an ex-Googler who has worked across Africa, Europe and the Middle East, where he had a variety of roles. During his seven-year tenure, he completed a Masters Degree in Analytical Psychology. He’s now back in South Africa and focusing his attention on the intersection between psychology and technology, with the aim of improving mass job placement and skill development for entry-level workers. This talk was given at a TEDx event using the TED conference format but independently organized by a local community.

Artificial Intelligence (AI) is evolving rapidly. Technology has created revolutions in productivity and the economy. But the pace of technological change and related job displacement is a new issue.

Historically, new technological revolutions have served the majority of people, even when some job categories are lost.

In the present day, since the solution to job displacement has yet to manifest, once again, the future of society is uncertain.

If you’re predisposed to view this unfolding uncertainty as threatening, then it will be disturbing news.

On the other hand, some will see opportunity in the changing landscape and embrace it with optimism.

Time will show one of those paths to be more productive.

This video represent a list of top 5 challenges that are common to many in the machine learning and data science community.

• Scarcity of Data

The more data that can be used for modeling and predictions, the better.

This isn’t a problem for big companies, such as Facebook and Google. However, for many others, lack of sufficient data can limit their results rendering machine learning less productive.

• Unclear Question

Vague questions will not result in substantive results. Data science is about recognizing patterns. So, clear questions are fundamental to defining what types of patterns to analyze.

• Unclear Representation of Data

For a data scientist, the resulting work needs to be represented to the end user in meaningful ways. There is more room for libraries to make life easier to better represent data.

• Expensive Resources

Computing millions of lines of code over and over can be expensive. But it should get less expensive in the future.

• Machine learning Algorithm selection

Currently, the biggest challenge in data science is the selection of the right algorithms. It’s important to understand the algorithms as well as possible to determine which ones will most benefit your project.

Machine learning is recognizing patterns in data and can help people support decisions.

Some practical ways that media and entertainment companies can apply artificial intelligence and machine learning is with contract management processing, budgeting, on-boarding production freelancers and standardization of digital asset management.

In brief, machine learning can help make an institution’s accumulated knowledge accessible to stakeholders.

The future of artificial intelligence (AI) encompasses risks and challenges.

The public perception of AI includes a fear that artificial intelligence might take away their job. And it’s true: AI is a tool people can use to operate business more efficiently. Of course some new jobs are created to support AI systems, but more people will lose jobs than new jobs are anticipated to be created.

Other risks for AI include data privacy concerns, government laws and regulations, and company stakeholders who don’t understand the technology.

AI is moving forward in spite of the challenges, but how smooth the transition will be for such an important shift in global technology and human labor is undetermined.

This NVIDIA video describes deep learning as the fastest-growing field in artificial intelligence, helping computers make sense of infinite amounts of data in the form of images, sound, and text.

Using multiple levels of neural networks, computers now have the capacity to see, learn, and react to complex situations as well or better than humans.

Specific real-world deep learning applications include the ability to analyze in one month, what used to take 10 years; voice-to-text technology; robotics; autonomous cars; and winning chess against a world champion.

Every industry will be impacted by deep learning, and many businesses are already delivering new products and services based on this new way of thinking about data and technology.

In this short video from Qualcomm, artificial intelligence is defined as “techniques that help machines and computers mimic human behavior.”

Artificial intelligence, machine learning and deep learning are differentiated and depicted as making our devices more useful than just utilities.

Examples applicable to smartphones, healthcare and autonomous cars are presented to show how AI can be used for improving life.

Every day, a large portion of the population is at the mercy of a rising technology, yet few actually understand what it is.

AI is designed so you don’t realize there’s a computer calling the shots. But that also makes understanding what AI is — and what it’s not — a little complicated.

In basic terms, AI is a broad area of computer science that makes machines seem like they have human intelligence.

It includes programming a computer to drive a car by obeying traffic signals.

The term “artificial intelligence” was first coined back in 1956 by Dartmouth professor John McCarthy. He called together a group of computer scientists and mathematicians to see if machines could learn like a young child does, using trial and error to develop formal reasoning. The project proposal says they’ll figure out how to make machines “use language, form abstractions and concepts, solve kinds of problems now reserved for humans, and improve themselves.”

But during the past few years, a couple of factors have led to AI becoming the next “big” thing: First, huge amounts of data are being created every minute. In fact, 90% of the world’s data has been generated in the past two years. And now thanks to advances in processing speeds, computers can actually make sense of all this information more quickly. Because of this, tech giants and venture capitalists have bought into AI and are infusing the market with cash and new applications.

When it comes to AI, a robot is nothing more than the shell concealing what’s actually used to power the technology.