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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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Smart data strategy essential to reap benefits from ai


AI’s Growing Role

AI technologies, driven by machine learning, are gradually transforming every industry, right from customer inquiries managed by natural language processing systems to robots in manufacturing. This is aided by exponential increases in computer processing power and volume of data creation. These effects are expected to only magnify in the coming decade as organizations transform their core processes and business models to take advantage of machine learning.

Moderate Adoption Rate

But a recent MIT Sloan study of business leaders has shown a substantial difference in numbers between those who believe in AI’s potential for change and those who have actually incorporated it in business processes. One reason for this is the confusion and hype surrounding AI. Most current investments come from R&D divisions of companies like Amazon, Baidu and Google, while elsewhere one mostly finds projects in discussion or pilot phases.

CIOs, AI and Data

Another reason is the added layer of complexity brought on by AI for CIOs already grappling with the disruption caused by digital transformation. Of course, AI can aid digital transformation, provided the data strategy from CIOs is robust. The key to effective use of AI is data that is accurate as well as meaningful and high-quality. Machine learning processes rely on the quality of data and metadata available to train the AI. So, leaders will have to invest in talent and information infrastructures, while laggards struggle with analytics expertise and easy access to data.

An important point to note is that, in most cases, AI for enterprises won’t mean entirely new applications. Machine learning techniques will be incorporated into platforms, products and services already in use in order to improve analytical power, data management and overall performance. So, a firm’s data analysts will not be replaced by AI systems, but will see their productivity and effectiveness improve.

In this environment, along with planning out their organization’s data strategy, CIOs will face two additional important tasks. This is the right time for them to educate the company leadership, and the organization in general, about the myths and realities of AI. And to complement the tools necessary to ensure quality data, they will have to put the right team in place to train AI algorithms.

So, as data-driven digital transformation continues to disrupt industries, CIOs will continue to play the role of agents of change. A data strategy that enables their organization to rapidly adopt AI will accelerate the pace of this change and ensure competitive differentiation.

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Deep image reconstruction – visualizing human thought using ai


Machine learning has previously been used to study brain scans (MRIs) and generate visualizations of human thought in case of simple geographic shapes or binary images. Past methods to reconstruct an image seen by a person have assumed that an image consists of pixels or simple shapes. But it is known that our brain processes visual information hierarchically extracting different levels of features or components of different complexities.

Deep Image Reconstruction

New research by four Kyoto University scientists uses neural networks as a proxy for this hierarchical human brain structure. Using the scientific platform BioRxiv and deep neural networks (DNN), the new technique lets thoughts involving sophisticated images to be decoded by a computer, producing images remarkably close to what a person is thinking.

Method

Over 10 months, three subjects were shown natural images, artificial geometric shapes and letters for varying periods. Brain activity was measured either while the subject was looking at an image (Experiment 1) or later, when the subject was asked to think of an image shown earlier (Experiment 2).

Once the brain activity was scanned, a computer reverse-engineered the information to generate visualizations of the subject’s thoughts.

Interestingly, visual imagery could be reconstructed, albeit to a lower accuracy, even in Experiment 2. This is possibly because it is more difficult for a human to remember an image exactly as it was seen.

Potential Applications

The mind-boggling range of possibilities, as the accuracy of this technology improves, includes:

·       Making art by imagining something

·       Visualization of dreams

·       Visualizations of hallucinations of psychiatric patients

·       Communication using thoughts

Other Research Activities

The Japanese researchers aren’t alone in this seemingly futuristic work to connect the brain with computing power. A former GoogleX-er is working to build a hat that could make telepathy possible within a decade, while another entrepreneur is working to build computer chips to implant in the brain to improve neurological functions.

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