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Reinforcement learning – from playing games to driving cars


What is Reinforcement Learning?

It is an approach to artificial intelligence that gets computers to learn like people by doing things incrementally well, without explicit instruction. The author gets a taste of this at a Barcelona AI conference where self-driving cars in a simulation learn to maneuver through a complicated four-lane highway by practicing moves over and over. This is also the approach that helped AlphaGo, designed by Alphabet subsidiary DeepMind beat the World No. 1 Go player in 2017 – Go is a notoriously complex game, making this achievement particularly remarkable.

Reinforcement Learning – A Historical Perspective

Psychologist Edward Thorndike first documented this principle over 100 years ago with an experiment involving cats who learned to escape from a box after stepping on a lever by chance. An early reproduction in machines was in 1951, where a machine simulating a rat escaping from a virtual maze saw some synaptic connections strengthen to reinforce successful behavior. Following decades saw intermittent successes, but using this for more complex tasks became computationally impractical.

The Breakthrough of Deep Learning

In recent years, reinforcement learning has become so formidable because of breakthrough in deep learning, which uses a large simulated neural network to recognize data patterns. This makes storing values for every move and updating them – the basis of reinforcement learning – easier.

This breakthrough is also what made DeepMind first excel phenomenally in Atari video games in 2013, resulting in its acquisition by Google. Today, Google is using these capabilities to make its data centers more energy efficient.

Reinforcement Learning in Self-Driving Cars

This approach is particularly well-suited for simulating humanlike behavior in self-driving cars. Today’s driverless cars often falter in complex situations that involve interacting with human drivers, such as traffic circles. To prevent extreme behavior – taking unnecessary risks or being too hesitant – these cars will have to acquire nuanced skills.

Mobileye, the Israeli firm that developed the Barcelona demo, plans to test the software on a fleet of vehicles with BMW and Intel this year. Google and Uber are already testing reinforcement learning for their vehicles.

While reinforcement learning helps automated driving by enabling good sequences of decisions, which is more efficient than pre-programming all such decisions, there are challenges too. The huge amount of data, the time needed to practice simulations, multiple objectives (avoiding accidents vs. keeping roads safe for other cars) – all make this an extremely complex area.

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Ai managers overview – opportunities & challenges


Artificial Intelligence has developed enough in the last few years to offer applications beyond relatively simple tasks. But a general AI system, which can solve many types of problems and is self-aware, is still not available. Even within the scope of narrow AI, where machines use abilities like deep learning and natural language processing to solve very specific problems, what new developments mean from a manager’s perspective are slightly different from what they do for a general user. The business aspect of AI can be considered with the help of three important questions:

1.     What is AI?

As computers lowered the cost of arithmetic, AI has the ability to lower prediction costs, i.e. generating new insights from existing information. But, judgment will remain a human forte for now, e.g. AI predicts the best fit candidate for a job position, while a manager handles mentoring and taking ethical decisions. In view of this, not just managerial skill sets will have to adjust, but other changes will occur too.

2.     How will AI influence business strategy?

  • ·       Increasingly more complex prediction problems will be handed over to AI systems.
  • ·       Managers will have to train workers in judgment-related tasks.
  • ·       To properly time the shifting of workforce training, rate and direction of adoption of AI technologies will have to be continually assessed.
  • ·       Processes that combine the strengths of human workforce and AI will have to be developed.
  • ·       Strategic alliances to access effective data will have to be formed.

 

3.     What are the major management risks from AI?

  • ·       Replacement threat – Some jobs may no longer be necessary as reliance on AI systems grows.
  • ·       Dependence threat – Prediction dependence on algorithms could create new vulnerabilities and potential inefficiencies, especially if errors and biases creep into these algorithms.
  • ·       Security threat – Protecting sensitive corporate information will not just be an IT problem, but a responsibility of the management too.
  • ·       Privacy threat – As more data is collected from the labor force, new questions of autonomy and freedom will have to be addressed.
  • Conclusion

The net effects of the strategic implications of AI on management, along with related risks, are uncertain – what is certain is that AI will change business. Understanding this evolution will help managers be better prepared for the business world of tomorrow.

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What is the fourth industrial revolution?


Ubiquitous, mobile supercomputing. Artificially-intelligent robots. Self-driving cars. Neuro-technological brain enhancements. Genetic editing. The evidence of dramatic change is all around us and it’s happening at exponential speed. Previous industrial revolutions liberated humankind from animal power, made mass production possible and brought digital capabilities to billions of people. This Fourth Industrial Revolution is, however, fundamentally different. It is characterized by a range of new technologies that are fusing the physical, digital and biological worlds, impacting all disciplines, economies and industries, and even challenging ideas about what it means to be human.

 

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10 important hr trends to leverage artificial intelligence


Transforming the Workplace

The effects of AI are just starting to be felt in the workplace. Its implications for HR leaders can be observed through a number of trends, the most important of which are:

1.     Humans working alongside AI – AI systems, like chatbots, are turning into team members, assisting human members and letting the latter on more complex, creative tasks.

 

2.     Identifying roles that will be transformed by AI – Very few job roles will see complete automation in the future, while most will get re-defined and shared between AI and human employees. So a key role for HR will be to develop a strategy that achieves the right balance.

3.     Transforming talent acquisition – Machine learning can help candidates personalize their job search. Companies can also do away with broad filters like education levels and focus on critical core skills to identify candidates who can move across business domains.

 

4.     Machine learning helping employees navigate their careers – Despite slowdown in upward mobility through promotions, employee retention can be improved by personalized career mobility platforms that help employees choose what skills they want to develop.

5.     Hackathons to identify new HR solutions – HR functions are working with software designers to try out innovative ways to solve age-old issues, like a platform to better match employers with the right job applicants.

6.     Using AI to be a virtual coach – Chatbots can be used to provide individualized coaching in the workplace as well as reinforce learning after a traditional training program.

7.     Enhancing employee communications – To mirror a seamless customer experience, AI can also be used to answer a range of queries put forth by employees. This turns chatbots into HR team members that allow employees to easily retrieve answers to questions at any time, whether they are mundane or extremely personal.

8.     Changing of leadership roles as AI moves into management – AI systems can handle several tasks like organizing meetings and taking minutes. This can assist mid-level managers in becoming more agile in decision making and letting them look at alternative scenarios with multiple points of view.

9.     Investing in the human experience – The most critical component in successful implementation of AI at the workplace is creating a more human experience, where employees get to know each other, and grow throughout their tenure with the company.

10.  Identifying new roles created by AI – AI will create more jobs than it eliminates in the next few years. So HR leaders need to prepare for a world where AI makes some jobs more efficient as well as creates entirely new ones. The latter could include roles like an IT facilitator or an AI trainer.

 

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The 2 key ingredients for success in ai application in the enterprise


The revolution that autonomous vehicles are expected to bring about in transportation, infrastructure, town-planning and energy distribution, has led to growing partnerships between auto and tech companies. PwC has proven its capabilities in the field, with its work in partnership with a Fortune 100 automaker earning it the Alconics Award for Best AI Application in the Enterprise, beating better known finalists.

The innovative solution developed by PwC used simulation and reinforcement learning, instead of more conventional machine learning or natural language processing methods, to address strategic questions related to launching a successful ridesharing network. The project teaches individual vehicles to perform collaboratively as a fleet, making decisions taking into account factors like city maps, customer wait times and charging locations.

The Strategic Implications of AI for Enterprises

The PwC project worked because, in partnership with a client that understood the nuances of AI, appropriate methodologies were used that were grounded in business use cases. Asking simply how to use AI is the wrong question; the right query is how best to approach existing issues, and choosing machine learning over traditional models if data is available and forecasting is most important.

Absence of this pragmatism raises the risk of disruption from a competitor offering the same service with lower cost and higher quality using the right AI solutions. As AI becomes a standard factor across the board, strategic advantage will come from prioritizing initiatives that provide value to the consumer. AI can be used to simulate markets or competitive dynamics, especially in completely new domains where historical data is unavailable, to improve decision making. Even in old domains, changing consumer usage patterns can inform the creation of better product and service offerings with the help of AI.

This works like what the tech community calls the ‘data flywheel’ – a positive feedback loop where customer-generated data, when analyzed by AI, increases value and greater value then increases usage. In the right conditions, this can generate explosive growth, and AI will be most important in markets where the flywheel can be strengthened.

Data Strategy and Architecture: The Most Important Ingredients

Beyond talent, data – in large volumes, of high quality and aligned to consumer priorities – is probably the most important ingredient for an AI solution, helping companies stave off competition. Such data requires deep domain expertise. Projects can fail to deliver if a solution, while technically sound, does not effectively address the business question. But, equally importantly, a clear vision without the support of talent, techniques and data doesn’t work either.

An effective, baseline architecture should include data management tools to maintain data sets as well as intermediate analysis results. It also needs the infrastructure to run models at scale and tools that provide data scientists with the flexibility to experiment with multiple techniques. Finally, there needs to be a visualization layer to enable data scientists to effectively communicate results.

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