Tag: AI

AI will eliminate many jobs. It will create new ones.

Mercer President Julio Portalatin and Philip Morris International CEO André Calantzopoulos discuss the changing global economy and the impact artificial intelligence is having on the workforce.

This video posits that humans are on the verge of representing a minority of available knowledge, compared to the potential of superintelligent artificial intelligence.

Narrow AI is created to solve one task, such as playing games, speech recognition, suggesting songs or purchases, etc. It does this well.

Machine learning is an attempt to mimic the way humans learn: observation and gaining more experience and knowledge.

Neural networks take in info and provide an output. They are computing systems vaguely inspired by the biological neural networks that constitute animal brains. The neural network itself is not an algorithm, but rather a framework for many different machine learning algorithms to work together and process complex data inputs

Artificial General Intelligence (AGI) is AI with more than a single purpose. It’s an attempt to mimic human intelligence. AGI is the intelligence of a machine that could successfully perform any intellectual task that a human being can. It is a primary goal of some artificial intelligence research and a common topic in science fiction and future studies.

Technological singularity refers to such an advance in AI that there is an explosion of new intelligence, which may not be understood by humans.

Superintelligent AI is different from software we know today, which we program and which follows our rules. With advanced AI, there’s a point where it doesn’t need humans.

WISDOM AND INTELLIGENCE

Intelligence is more about making mistakes and acquiring knowledge and solving problems through that.

Wisdom is about applying the correct knowledge in the most efficient way. Wisdom is being able to see beyond the intelligence gained and being able to apply that to other things.

Superintelligent AI is the last invention mankind will make. Once it’s invented and it cannot be uninvented. It could be good or bad.

The video concludes by suggesting the viewer learn more at Brilliant.org

This video includes highlights from The Future of Leadership Development Conference Series, held April 19-20 2018.

The current technology revolution is reshaping industries, making business models obsolete, growing new companies with a different set of capabilities and creating disruption and social change.

In a digital, big data, machine-learning, robotics and artificial intelligence-based world, the role of general managers is more important than ever. The competencies that they need are changing fast.

At the same time, there are some classical attributes of the general managers’ functions – providing a sense of purpose, developing a long-term perspective, and engaging people and making teams functional, among others – that are still relevant, but that may take new dimensions in this new, changing business world.

What companies, people and society in general will expect from senior managers in a few years’ time will be different from their current skills and capabilities.

At the same time, technology is disrupting companies and communities, CEOs, board members and general managers, as the ultimate stewards of a company, need to reflect on how to manage this process and help come up with constructive solutions.

The purpose of this conference was to discuss these relevant issues for leadership, governance and management with an inter-disciplinary perspective. Speakers include leading management and leadership scholars, AI experts, CEOs and senior general managers, and deans of leading international business schools.

Some of the specifics that were covered include:

What creates long term value? That would be customer-asset building and brand-asset building since, so far, computers cannot build brands.

The parts of business that will change:
1) What they make
2) How things are made, or how businesses operate
3) Who businesses will form alliances with
4) How the people work are organized and managed.

Businesses and be broken down into projects and processes.

Processes will become an algorithm

We need to teach people how to use data.

Data and AI are important to all business units.

Management needs to know the right questions to ask the technical guys.

Decisiveness wins out over building the perfect model.

The most important ingredient for success is the power of imagination, which cannot be substituted.

AI can augment human intelligence, not replace it.

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.