Regulating emerging technology is a slow process and difficult to keep up. Each proposed rule must be written, reviewed, re-reviewed, edited, debated, and voted on, and then, depending on whether the vote passed or failed, enacted. New discoveries occur at a frantic pace, changing the field and moving the world in new directions. In many ways, it is like shooting at a moving target.

Existing AI Regulations

For a technology as broad as AI, it is going to be a while before regulations catch up. There are some in place such as regulations on personal data and storage of personal information, but even these are in their infancy.

And, these current regulations do not specifically govern AI but rather the data on which AI relies. Largely centered around the collection of ‘big data’, specifically personal data, regulations have existed for years regarding end-user-licensing-agreements, those pop-ups that appear when you install new software that most users just click “ok” to accept and move on. Somewhere, buried in the text, is an agreement on the software’s ability to collect on your usage.

More specifically, privacy laws and laws requiring companies to notify us when they are gathering and using our personal data have become more prevalent in recent years. The EU has enacted laws that require websites to notify EU residents when personal information is gathered and saved. Like EULA agreements, we now just click the annoying pop-ups that say that cookies are being used by a website.

Regulations For AI

There are some who argue that, in the interest of protecting consumers, regulations are speed bumps slowing progress. Others will argue that without regulation, it’s the wild west and destructive uses of AI will ensue. Regardless, AI is going to be an emerging technology pushing the boundaries with few, if any, rules and regulations to guide not only what can and cannot be done, but what should or shouldn’t be done with the power of AI.

For example, there are few, if any, regulations regarding the use of AI to make fake videos, though some social media sites are taking measures to limit the distribution of these videos. Also, while there will be safeguards holding back self-driving cars or self-landing planes, there are surprisingly few regulations for apps or devices gathering data even when we are not using the app. For example, some voice-activated assistant devices will not tell you if they are storing your personal data or listening in, and regulations to limit or prevent this are just being drafted.

What To Expect

Some regulations will result from a misuse of AI, and some will already be outdated upon their release. However, as more and more personal data is gathered and as AI becomes more powerful and pervasive in our lives, we should expect regulations governing the use of AI -- and that they will be predictably behind the times.

Envisioning a future run by AI is just the beginning. We can picture it, a world connected and running seamlessly by machines. We dream of easier times when there is less labor and more time to enjoy our lives. We seek a world in which AI relieves our burdens and makes the world a better place. But, can we make it a reality? What are the next steps? 

In An Ideal World

In this future of AI globalization, all the nations of the world would collaborate and integrate AI. Travel, trade, science, health, economics, agriculture, and more would smoothly operate across the world, improving lives everywhere. AI programs would communicate and manage all the processes required to run the world, and we would have all the time in the world for more intellectual, personal, and cultural development.

Ideally, AI could integrate the systems of many nations into one, managing the system without regard for politics. From balancing inventories to allocating resources, it would be possible for artificial intelligence to do the work of all nations as one entity.

Now, here is where the ideal world comes crashing down; for AI to run the world, there must be a level playing field, and that is not realistic.

The Real World

Human nature is competitive, and we vie for collecting the most resources to be the strongest and the most powerful. As civilized as we are, we are ruled by basic instincts. It may not be possible for all societies to overcome this instinct for power and accept a level playing field.

Nations will face difficulties sharing information and participating with other countries. Questions like who owns the data or controls the system will matter.

These roadblocks along with varying Internet protocols, server management strategies, and user regulations are difficult to navigate from one nation to another. Concerns for compliance and national security could prevent globalization for AI.

What Will It Take?

Still, some precedents exist that may provide guidance for collaboration between nations. The development of the Euro, recent popular international communication apps, and search engines reaching world-wide lend optimism to the possibility for international collaboration.

Then, there are the practical steps towards massive, worldwide AI. All nations must have an infrastructure capable of producing and managing artificial intelligence. From the hardware to the software and trained programmers, nations will need to have the right equipment, power, and resources to run the systems.

Most of all, we need reliable, proven AI. As artificial intelligence evolves from automation and augmentation to autonomous, we’ll gain more confidence in its abilities and discover more applications to harness its power. Once we realize all the potential for AI and usage gains momentum, there will be a natural push to stay competitive, even if that means finding a way to integrate and collaborate with other nations.

Possibility V.S. Probability

While a future in which we experience globalization of AI may be possible, as of yet, the probability is less likely. The technology will probably exist long before the capability for nations to collaborate.

The ability for artificial Intelligence to take massive amounts of data from different sources, correlate it, manage data updates, and draw conclusions makes AI a vital tool in tackling climate change. It has been an ideal resource for predicting and forming conclusions based on nothing more than objective information.

A Complicated Study

Scientists studying climate change work within a given region as it pertains to one or more ecosystems, species of animals, etc. From the artic, the Alaskan wilderness, wildfires in Australia, bleaching of the coral reefs, plastic in the ocean, to any other known deleterious effects of humans on the planet, scientists are busy finding critical puzzle pieces to determine the causes and solutions for climate change.

Each study fills in part of a picture that can only come into focus when everything, all the data, gets compiled, analyzed, and debriefed. Thanks to AI, science can finally assemble the pieces of the puzzle into a coherent picture.

Artificial intelligence programs take the disparate scientific study groups and assemble all their data in a meaningful way. Whereas humans accomplishing the assembly would inherently build in errors or truncate data to make it manageable, AI can take these data points from hundreds of different studies and put it all together.

An Exhaustive Resource

The power of AI is the ability to show issues on a global scale. Data ranging from regional studies of air, water, ice, and other biomes reveal a global picture when processed together. Now, AI can offer perspective to help answer whether regional anomalies are indicative of a global trend towards a warmer world or not.

And, AI can continually revise and update its analyses with additional data from new or ongoing studies. AI can seamlessly integrate new data as it is collected from all over the globe. This allows almost real-time projections as this data is refined and incorporated, allowing adjustments to models of climate change and more accurate predictions.

A New Playing Field

While AI is being used to better predict the future of climate change, it can also be used in the mitigation of harmful effects contributing to the very changes that are being predicted. Climate change is driven by excessive CO2, carbon dioxide, and other greenhouse gases emitted by industries worldwide. AI is playing a crucial role in allowing these industries to continue operating, while limiting the amount of harmful gasses being released.

Using AI, power distribution can be optimized within a much shorter time frame for power grids. AI reduces the drain on power grids by determining demand within a short time frame compared to previous calculations yielded by studying long-term trends. This not only reduces greenhouse gasses produced by the power plants, but is financially beneficial as well, making it attractive to implement for everyone.

Tipping The Scales

Data produced by AI gives us a global advantage. By leveraging AI, scientists and industries alike finally have the capability to combat climate change.

AI has been incorporated into everyday life thanks to smart devices, but the next quantum leap will be much more integrated. AI is set to replace whole infrastructures that we have known for years.

Transit. Self-driving cars are on the cusp of appearing on the roads, but that is not the sign that we have achieved integrated AI. The paradigm shift will really be when we don’t have a few self-driving cars on the road but when all the vehicles are self-driving units.

When AI controls the cars in unison, instead of each one thinking for itself, the whole way that we move around will transform, and mostly for the positive; stop lights, speed limits, traffic jams, and speeding tickets are all artifacts of our human inefficiencies in reaction time, impulse control, and self-interest. A self-driving network will allow for seamless integration and interlacing of traffic to maximize efficiency while minimizing risk; faster transit times with less accidents.

Domesticity. Smart homes are more regularly advertised on the market as using AI operated devices, utilities, and appliances. Another smart home concept operates all the devices from one central device that can control the house from the security alarm and garage doors to the television, stove, and even the showerhead. All this home integration, however, comes after the house is bought, so the next level of integration will be homes built as ‘smart homes’ from the ground up.

AI built into the walls and lights and floors of our homes would alter our way of life. AI systems could provide power where we need it and cut off energy to areas we don’t, eliminating unnecessary electricity waste. These systems could monitor who is where in the house to ensure that the right room is lit and at the right temperature, turning the lights off as we leave a room. More importantly, smart homes could also use this ability to constantly monitor the health and activity of the occupants, knowing immediately if there is a medical emergency, a break in, or a teenager trying to sneak out, or in, through a window.

Retail. Smart technology is also primed to integrate into 21^st century brick and mortar stores. In some ways, this is already the most advanced integration, even though we don’t see it. AI has integrated so thoroughly with our phones that stores know our locations, e.g. when we enter their establishment down to the aisle or sector we’re standing in. Stores then tailor our web browsers to views of that portion of the store, providing ads from them that appear when we access the internet.

Another level of integration will appear and replace self-checkout, which is only the first step in automation at a store. The next step will be no checkout line at all. This could happen when AI monitors our movement in the store, analyzes what we put in our cart at the time of pickup, scans our cart periodically as we go, and just auto-debits our account as we leave the store without ever having to stop at the checkout aisle.

“Meet George Jetson!”

If we throwback to The Jetsons, an old cartoon from the 80s, we see far-fetched ideas like home robots and a society transported by self-driving vehicles. Today, those concepts are coming to life. The ability for AI to integrate further into our lives is coming, altering our habits and lifestyles to become a reality only previously imagined.

 Model N, Inc. (NYSE: MODN), the leader in cloud revenue management solutions, today announced a range of new features as part of its Spring 2020 high tech product release. Model N’s new platform capabilities leverage artificial intelligence (AI) and machine learning (ML) to enhance channel automation, empowering high tech companies to make real-time pricing decisions that align directly with corporate revenue objectives.

The global coronavirus pandemic is creating economic pressure across multiple sectors, including high tech and semiconductor, to find new ways to maintain revenue growth and channel performance. Under this extraordinary circumstance, some 37% of companies have reported canceled orders and cash flow issues, and 18% of firms have reported supply chain disruptions, in a recent study surveying channel chiefs, “Evolving Impact of COVID-19 on Vendor Channel Programs,” conducted by business strategy and research firm The 2112 Group.

“Forward-thinking companies are reallocating budget expenditures and evaluating strategic technology investments. This tactical planning is aimed at weathering the current economic downturn and accelerating the post-pandemic recovery for success beyond the curve,” said Larry Walsh, CEO and chief analyst of The 2112 Group. “Many firms are driving improved financial results with automation and digital channel platforms for more active and efficient revenue management. These investments include solutions like Model N that automate and monitor quoting, pricing and payment transactions that increase topline revenue at a time when it really matters.”

Upgrades to Model N’s cloud-based revenue platform are designed to help address revenue management issues for tech companies – as 89% of executives say they’re losing billions of dollars through revenue leakage, and 82% say that revenue management has become increasingly difficult. The Model N Spring 2020 release delivers new intelligence and automation capabilities that provide real-time decision insights for business users. With new capabilities leveraging AI, Model N optimizes price recommendations, allowing approvers to improve win rates. Additionally, sales teams can now easily predict expected quote volume compliance based on contract and quote terms.

Model N’s updated automation capabilities now better link manufacturers and their channels, supporting companies in their ongoing quest to learn more about channel sales revenue. Created to help Model N customers realize topline revenue growth, these new capabilities include:

• ·Channel Data Management (CDM) – new communication options enabling exchange of data that streamline and reduce workload, increasing partner compliance
• ·Market Development Funds (MDF) Management – new support for managing complex workflows across an organization, integrating intelligent approvals with MDF budgets
• ·Rebate Management – empowers manufacturers to better model and manage the increasingly complex incentives required to influence partner buying patterns

These new innovations are integral to revenue strategies being adopted by some of today’s most influential high-tech manufacturers and semiconductor companies. “During the past 18-24 months, large tech companies have embarked on a new quest in revenue management, focused on introducing AI and ML functions that impact net price and net revenue as a single continuum,” said Suresh Kannan, Model N Chief Product Officer. “The desire to equip our industry-leading customers with the latest tools that support their revenue success strategies is precisely why Model N has delivered this release.”

Tech company believes they can provide contact tracing and still maintain people’s privacy by using AI, reports Tech Wire. 

Believing that artificial intelligence can improve so-called “contact tracing” and also protect people’s privacy in the battle against COVID-19, Raleigh-based startup Diveplane is rolling out a program called “Aware.”

Read the article on Tech Wire

According to Sci Tech Daily, researchers are using artificial intelligence to study how lithium ion batteries degrade. 

New machine learning methods bring insights into how lithium ion batteries degrade, and show it’s more complicated than many thought.

Read the article on Sci Tech Daily

According to Wired, Walmart’s anti-theft technology relies on artificial intelligence that is often faulty. 

In January, my coworker received a peculiar email. The message, which she forwarded to me, was from a handful of corporate Walmart employees calling themselves the “Concerned Home Office Associates.”

Read the article on Wired

Like several other banks in the EU reeling from the impact of the 2009 financial crisis, our client Bank (name withheld for confidentiality) had to take measures to restructure the non-performing loans under a parliament-approved securitization scheme. The Bank uses its in-house call center to contact non-performing loan debtors and negotiate offers to restructure the debt according to the debtor’s unique situation and their ability to pay-out the loan.

Given the effect of non-performing loans (NPLs) on its bottom line, the Bank was seeking a technology partner to help it optimize the collections process. Behavioral Signals partnered with this Bank and applied its AI-based agent-customer matching technology towards maximizing the effectiveness of their Call Center. 

AI and Finance

Financial institutions are leveraging AI systems in their contact centers for improved efficiency in delivering service to customers, helping them engage the customer uniquely. The ability to route and measure agent-customer interaction offers not only a higher probability of achieving the desired result, be it a sale or collection, but also insights on how AI is impacting the whole communication process.

Behavioral Signals  proposed a model to optimize the call centers’ outcomes by using machine learning techniques to route the  caller to the best-matching agent. By creating agent and customer behavioral profiles, from their past audio interactions, we were able to create a predictive model on which agents’ skills and overall behavioral traits during the calls are best suited for the specific customer in order to achieve the desired result.

From Voice to Revenue

The solution was deployed on Live-Data in a champion/challenger setup demonstrating a significant return-on-investment with an actual increase of active debt restructuring applications by 20.1% in total. On top of that, this improvement was essentially achieved with 7.6% fewer calls (Right Party Contacts), leading to additional cost reductions. In absolute numbers, these results actually correspond to approximately $7.5M USD of additional restructured debt for the Bank over the course of this evaluation and a potential $300M USD annual upside.

Next-Gen Banking

Contrary to a common concern, this drastic improvement of collection outcomes did not compromise the customer’s satisfaction levels. The Bank noted that these interactions have been smoother than average with the majority of interactions rated very high by the Bank’s QA team, clearly underscoring an improved ability of the agents to build rapport with their clients. Furthermore, these interactions provide excellent examples of successfully-handled interactions that can be used in training/ coaching of new and existing agents, thus improving the performance of the call center as a whole.

The Bank is extending the scope of cooperation with Behavioral Signals to deploy the Behavioral Profile Pairing (agent customer matching) application to the entire debt collection team along with other applications, to improve the efficiency and performance of its call center. 

It has now become common hearing people talking about how artificial intelligence (AI) will change various aspects and industries in every country on the planet. There are growing fears that this technology will eliminate many jobs and professions through automation of tasks. Many reports have shown the truth behind job automation especially for certain professions and demographics. For example, a report by Brookings Institution noted that automation threatens more than 25% of jobs in the US, most done by low-wage earners where tasks are repetitive and routine-based.

Despite these harsh realities, we have no option but to conform to the new changes and adopt these technologies due to the massive opportunities presented that far outweigh downsides. Although artificial intelligence will indeed make other tasks obsolete, it will also inject more jobs and change the existing ones for the better. Workers in different countries are now beginning to see the bright side of AI on their roles, and organizations and are slowly starting to change their minds on this technology. Most of them are now grateful for having robots as their coworkers because of how they simplify and streamline their lives.   

A majority of human workers now trust orders given by robots over those of human managers. Most of them are even turning to robots for advice instead of their human managers. In some companies like American Express, for example, tasks such as identifying product offers that are relevant to a specific customer segment are now made by AI, therefore taking over the role of managers and employees in handling these tasks.

AI is now taking over administrative tasks traditionally done by human managers while the roles of managers are now evolving as they focus more on soft skills over hard ones. Surveys have indicated that robots are far much better than human managers in doing tasks that require hard skills as they can easily give unbiased information, maintain work schedule, solve problems quickly and their budgets can also be easily managed. On the other hand, humans can complement them with better empathy. Unlike humans, robots can also provide answers to confidential information without causing fear among the interviewees. They can also evaluate performance of the team with no bias. Although robots and AI systems have advanced significantly, human managers still beat them in many areas. For instance, human managers can understand feelings of other humans better and can reliably provide oversight and direction.

Artificial intelligence collaboration with humans in workplaces is freeing up managers, giving them time to focus on other issues of their jobs. By cutting managers loose from their traditional tasks, AI is enhancing innovation. They allow managers to have more impact on their work by giving them time to create strong relationships with other employees that in the end impacts their roles.

Far from replacing managers, AI is improving their decision-making capabilities. The main change in decision making in management originates from the AI’s deep learning abilities. Machines through deep learning are becoming smarter with time as they continually learn from the experience of human managers just like the way managers gain their knowledge from interacting with other managers and encountering new challenges. With deep learning, AI promises to be part of decision making process in the coming days. Through the help of AI, managers can tap into the advice they can offer at any time due to large data they have. This will make managers smarter and more qualified to make informed decisions that can take a company to the next level.

From Super Bowl ads boasting the benefits of machine learning to rappers touting the advantages of artificial intelligence, nearly everyone knows about the cloud, how powerful it is, and the advantages it can provide for both businesses and consumers. Then you also have the pundits adding to the hype with absolute statements like this:

“If you have not developed a cloud-first strategy yet, you are likely falling behind your competitors” -Gartner

If you can find a flaw with that statement, then you’re ahead of the game.

Cloud-first is a brilliant strategy that has served as the de facto CIO direction for close to a decade. Nearly every CIO serving entities from the government to leading enterprises publicly embrace this approach. The cloud-first strategy has resulted in a predestined design where cloud-compatible data and applications find their way to the cloud to achieve efficiency and flexibility.

Cloud technologies are so powerful and beneficial that once an organizational cloud standard is in place, it is difficult to see favor in any other information systems. In so many ways, it has never been simpler to get on a cloud platform. One credit card swipe, and you’re on your way. As a result of these conveniences and benefits, we live in a world where public clouds, private clouds, cloud services, and a litany of cloud technologies help improve business power and add to the flexibility of organizations.  

A Cloud Wake-Up Call

Despite all the advantages of cloud computing, many organizations under a cloud mandate today find their technology, operating, and migration costs are not what they anticipated. Many IT departments have legacy applications that are not cloud-ready, plus significant shadow IT and overly complex cloud infrastructures are creating severe cloud sprawl. Other organizations can't build technology around their business initiatives because they don’t have the staff, or the necessary efforts are beyond their capabilities. For these and other cloud situations, a cloud-first approach is making matters worse.

For these and other situations, the cloud-first strategy has uncovered a whole new world of challenges: 

• The need for cost and consumption control tools 
• Complex compliance mandates and technical requirements
• Cloud staff acquisition and retention
• Increased security needs
• Lack of visibility and metrics
• Bifurcation of IT infrastructures
• Underperformance of legacy applications 

A New Cloud Phase

Hybrid and multi-cloud technologies have proven their effectiveness in the enterprise as the best path forward by adding functional co-existence to the informational technology picture and unlocking the potential for an application-centric cloud strategy. Cloud-leaning philosophies hold untold business values, but not every application out there was built with cloud technologies in mind. There are far more traditionally structured applications than not. Some applications might be better suited to one cloud service over a given cloud-based solution. Factors like security, integrity, auditing, and performance might become more complicated or costly within the constructs of a limitless cloud environment. Depending on your organization’s capabilities, some applications are simpler to leave and integrate through on-premise systems.

Application First

In this second decade of cloud, a new phase is emerging in which business advantages must be achieved by focusing on the user and taking an “application first” strategy rather than a cloud-first strategy. While public cloud and private cloud architectures have extremely powerful benefits, there are too many challenges to navigate when enterprises insist on solely implementing one versus the other. As part of a strategic architectural practice, hybrid cloud and multi-cloud approaches can properly shift the focus to the users and the type of applications they interact with.  

Being smart about the cloud means using its benefits correctly, according to your business, your users, and your applications. We can even go so far as to say that if you have not developed an application and user-first strategy as of 2020, you will fall behind your competitors. Once you’ve built an intelligent strategy, it’s time to move on to a discussion of platform and infrastructure advantages, such as edge computing, artificial intelligence, machine learning, hybrid, auto scaling—technologies that are only possible with the more flexible application-first approach within hybrid, multi-cloud solutions, leaving cloud-first as an antiquated ideology.

Emil Sayegh is widely recognized as one of the industry’s cloud visionaries and "fathers of OpenStack" (he launched and led the cloud computing and hosting businesses for HP and Rackspace), can talk about everything from rising decentralization, to hybrid and edge clouds, and the significant impact that AI and Blockchain technologies will have on the industry. 

Artificial Intelligence is slowly taking over our day to day lives - especially when it comes to the workplace and using tech to enhance and fast-track tasks. There are many sectors in which AI can help improve services - specifically national health services, banking and legal services. Not only do some of the biggest names in tech debate over the future of AI and how it continues to make life easier and better for many, but we are now seeing some of the biggest tech companies now recruiting heavily in AI.

But, who is recruiting for AI roles the most and is making big steps in looking toward the future?

With AI-related jobs more than doubling over the past three years and job postings related to AI increasing by 119%, RS Components has analysed job posts from some of the world’s biggest tech companies to discover who has the highest percentage of AI-related job openings.

Microsoft leads the way with 1,964 AI job openings, equating to 36% of the total jobs they have on offer. With Bill Gates being one of the world’s richest people still, this is no surprise - Microsoft still leads the way with some of the most innovative and thought-provoking technological inventions.

Nvidia follows in second place with 837 of AI job openings, equating to 33% of total jobs they have on offer. The computer game company designs graphic processing units for the gaming and professional markets, so using systems and analytics such as AI helps with processes and development of new games.

A former Google Exec has predicted that AI will replace 40 per cent of jobs in the next 15 years. The Google Exec stated that AI will allow entire new industries to be built from the ground up with automation in mind. AI will be cheaper than human employees, however, it will also create new roles, as well as replace current ones.

Popular social media platform Facebook and streaming platform Netflix make it into the top ten with Facebook offering 191 jobs in AI, 6% of the total, and Netflix offering 35, also making up 6% of the total jobs they have on offer. Both companies are growing rapidly and are having to set new trends to keep up with technology, so it's only right that they are in the top 10 companies that are hiring in AI the most.

The largest tech company in the world, Apple, comes in at number 12 with 129 AI jobs on offer, only 3% of their total 4,574 jobs available. Apple is one of the most innovative companies in the world - alongside Google - so it may come as a surprise that they are in 12th place. This could be due to the fact that they are forward-thinking and hired the majority of their AI roles a few years back.

Another innovative company in the technology business is Bezos' e-commerce platform, Amazon, which comes in 15th place offering 297 roles in AI, 2% of its total 18,781 jobs on offer.

It’s clear that the technology sector prioritises hiring in AI, but which sectors will be next to drive up the market and start boosting their recruitment in AI?

Take a look at RS Components’ interactive campaign here which highlights all of the companies which are recruiting in AI.

Top Ten Global Companies with highest % of AI Jobs:

Minecraft is a popular sandbox multiplayer online building game. It starts with a generated world where terrain and features are created with building blocks. There are also generated characters called “mobs” that appear throughout the world. The two types of gameplay in Minecraft are survival mode and creative mode. In survival mode the player can be harmed and has limited resources. In creative mode the player does not have to worry about being harmed and has access to all resources. In creative mode the player’s imagination is the limit and there have been endless creative projects that have been carried out in the game including recreations of cities and landmarks among others. 

CraftAssist is a framework created in 2019 that features dialogue-enabled interactive assistants. These assistants collaborate with human players in the game. The assistants can build, move objects, spawn mobs and communicate with players by chat. The framework is intended to research how agents interact with players and discover the possibilities between them. The framework is open source to encourage AI researchers to use the toolset. 

Players can interact with other players and the AI assistants, just like in a regular game of Minecraft. CraftAssist includes a baseline assistant, who brings over intelligence of previous interactions. There is also a house dataset that was put together using crowd sourced participants. These participants were tasked with constructing a house within 30 minutes and their actions were recorded. Both of these datasets are part of the core CraftAssist release.

Minecraft is played through a client/server architecture and the bot communicates through the Minecraft protocol. The Minecraft protocol does not require server side mods, which allows the bot to join in with other players and bots. The bot assistant is modular and this allows research to be conducted based on the individual models. These include modules related to perception and memory among others. The bot modules work together to perform tasks and interact with the human players. A human player may request that a bot build part of a house such as the roof. The bot then takes the chat commands and performs high-level actions that carry out the informal chat instructions to build the roof. 

Another example would be a player telling the AI assistant to build a house in the shape of a rectangle near the water. The modules take the given instructions from chat and create a program for the assistant. In this example the object water would be identified as being close and the assistant would be given coordinates to move to and start building. Machine learning works well for difficult and well defined tasks. One of the larger goals with CraftAssist is a framework that takes the often ambiguous human language commands and creates executable actions through the assistant. The interaction between humans and bots is a core focus of CraftAssist. 

More data from past interactions is being added to the assistant for further improvement. If you are a Minecraft player I would recommend giving CraftAssist a try to help build and improve the CraftAssist project!

CraftAssist Documentation:

https://research.fb.com/wp-content/uploads/2019/07/CraftAssist-A-Framework-for-Dialogue-enabled-Interactive-Agents-v3.pdf?

https://github.com/facebookresearch/craftassist

For decades, artificial intelligence has been seeking to mimic humans and nature. The main goal here is to create systems that have the ability to make decisions like humans. As such, most AI systems are brain-inspired, and scientists behind it have always attempted to mimic neural networks, a term borrowed from biology. With computers now able to think like humans, computer scientists are now focused on a different goal as they seek to revise artificial intelligence by transitioning it into smarter and more efficient systems. Initially, most of the research was devoted to training AI through machine learning to enable algorithms to learn how to recognize objects and how to perform basic tasks. Now that this has been achieved, these techniques are being deployed to create more advanced systems that can work autonomously without human intervention.

Google has been working on AutoML (automatic machine learning system), a smart machine learning system that can be mutated into a next-generation AI. This new system has been enhanced through the addition of the Darwinian evolution concept that allows building of AI products that can improve themselves. The product of this improvement is called AutoML-Zero. While this system can be worrying to many, if used well, it can open doors for the development of much smarter systems that can mimic human brains in development and decision making through enhanced evolution.

Going back to Charles Darwin’s concept of evolution, slight or random changes in the genetic makeup in an organism are either advantageous or disadvantageous. If the mutation allows the organism to survive and reproduce, the characteristics are then inherited by the future generation. Otherwise, the mutation dies with the organism. In AI, this phenomenon is known as neuro-evolution. It is a process in that neural networks recreates that built parts of the brain to allow only the strong or smart to survive.

An AutoML system makes it easier for applications to use machine learning and other automated features, unlike AutoML-Zero, that requires human input. It uses a three-step process that is setup, predict and learn to improve itself. The system begins with a selection of 100 algorithms by analysis of their mathematical operations. It then identifies the best of these algorithms through a trial-and-error approach. The best ones are retained and tweaked for the next round of trial-and-error while those found to be less effective are dropped. This is basically a mutation of neural networks. The process goes on and on until the neural network is highly efficient.

The new code is more effective and can efficiently identify things that have always been hard for AI to differentiate, such as the difference between objects. These best performing algorithms are kept in a similar manner as survival for the fittest fronted by Charles Darwin. According to researchers at Google, this Darwinian concept of neuroevolution can lead to the creation of an algorithm with 94% accuracy. Various studies saw an improvement in the algorithm’s image recognition skills.

The evolution of the machine learning algorithms, according to researchers, has led to the creation of more artificial intelligence systems that can work more like humans and without bias, that humans are known for. The mutation of algorithms will see the emergence of systems that can perform several tasks, including complex tasks that have for long relied on human expertise.

Despite the complex nature of the development of AI systems and advanced research that is needed to deliver highly efficient systems, Google’s AI mutation approach has shown to be a giant step towards making cutting-edge AI systems that can be helpful in the future. It is through such initiatives that we are likely to see systems that can outsmart humans in every aspect sooner rather than later.

While artificial intelligence (AI) continues making headlines and breaking barriers that were thought to be impossible a decade ago, humans are now looking at it with both awe and fear. This is understandable because no one can allow their work to be taken away or to be replaced in places where they have for years looked for their daily bread. When people hear that companies are building AI-powered assistants to help in different tasks, they get afraid that AI is close to becoming independent of humans. The questions that they often ask are; Is AI close to matching human intelligence? Are we close to the fictional Skynet predicted in the Terminator? Is AI smart in its own and will it take positions long occupied by humans?

Until a few years ago, humans seem invincible, and computers appeared only servants who cannot outshine the master. In the mastery of language, for example, computers were far second to humans in producing sentences that make sense. However, as the fields of machine learning and natural language processing (NLP) continued advancing, signs were written all over showing the capabilities that computers had in this area. The advances that have been made in NLP has seen computers getting closer and closer to humans as they can now generate complex passages with a click of a button. These advances are made possible by deep-learning algorithms that can pick statistical patterns in words used by humans and the structure of words from many lines of text. This is a step in the right direction. However, despite the worries of humans and the excitement by professionals that AI is close to matching humans in their language capacity, the truth is that computers are not there yet.

According to new writing done by Allen Institute of Artificial Intelligence, there is something that machines still lack, but humans have. Machines do not understand what they are writing or reading. This is the biggest flaw that is yet to be sorted out in pursuit of generalizable AI. Although this question applies to academia more, it also touches on the consumers of AI technologies. As organizations continue to adopt chatbots and voice assistants to help in their daily tasks, lack of adequate understanding of language by these systems is proving to be a  hurdle. This is because it slows access to crucial services mainly in financial institutions and makes these applications prone to failure.

Based on the Winograd Schema Challenge built in 2011 to evaluate the reasoning of NLP systems, it emerged that computers have a poor grasp of semantics and still needs training before they can match humans. The challenge is made up of 273 questions that have pairs of sentences that are identical except for one word known as a trigger. For a system to be regarded as fully intelligence, it must figure out the difference in two different sentences.

Although AI is progressing rapidly, its capability to pass the Winograd Schema Challenge or Turing test, that is designed to see if machines can mimic humans in everything successfully will still need help from humans.  Human touch is still essential for machines to attain full independence, and this will be the case for some years to come. Without help, every word that machines utter will have some type of emptiness and will always be robotic. Humans still have unbelievable levels of accuracy, that stand at 94% compared computers that have between 59% and 79% accuracy. This data will serve as a new benchmark and will inspire further research beyond machine learning.

Unlike humans who can have knowledge of two languages and can mix languages and still make sense, machines have some weaknesses doing so. Furthermore, humans can understand questions easily and choose the correct one accurately. With increased training and improvement in both hardware and software, AI systems will get closer to humans in future.

Artificial intelligence, machine learning, and big data are revolutionizing manufacturing, marketing and supply chain. The same is happening to scientific research where AI is proving to be a critical technology in scientific discovery. For instance, it is proving indispensable in predicting the outcomes of experiments, an example being quantum systems. Fully incorporated with fundamental laws of physics, AI can easily predict scientific simulations. Materials science, just like other experimental science branches such as drug discovery, leverage AI to model sophisticated experiments from data that is collected.

A deep learning algorithm developed by a collaboration of the University of Warwick, University of Luxemburg and Technical University of Berlin can now help predict molecules’ quantum states, functions, and determine all the properties of these molecules. AI does this by learning how to solve essential equations in quantum mechanics. After thoroughly learning this, it solves the equations with ease as compared to traditional methods that take a long time and require many computing resources. This new approach can deliver accurate results within a few seconds on mobile phones or computers.

Advances in artificial intelligence are likely to alter every aspect of human life, ranging from transport, business, and agriculture to healthcare. Similarly, science and research are also set to reap big with this technology as countries and institutions position themselves to take the lead. By mid 2020s, AI will be ubiquitous for carrying out experiments while computers will control the logging of data and instruments used in scientific research. This shift will not only ease scientific experimentation but will also enable an enhanced analysis of data that is obtained from experiments and even suggest what to experiment next. Currently, sophisticated AI systems are exploring reinforcement learning and Bayesian analysis.

Advancement in AI, mainly within the aspect of machine learning, is allowing scientists to automate processes by learning based on data that is collected during different experiments and respective findings. Through such learning, the cost of finding out a specific scientific phenomenon is reduced. AI systems can also be used to optimize the system to meet specific criteria. Through analysis of results in different experiments and the subsequent discoveries, researchers can alter their approaches, saving not only time and money while enhancing outcomes.  

Advanced equipment needed

Scientific AI techniques will also be applied in areas such as quantum technologies, nanoelectronics, and screening of materials and microscopy where computer vision will be required. Despite this, some types of AI and machine learning that will aid scientific discovery will require sophisticated computing hardware that is not currently available that includes Intelligence Processing Unit (IPU). Through these advanced IPU’s, new scientific innovations will be developed, and a new type of researchers that know how to capitalize on technology will emerge in science and engineering.

As climate change continues ravaging the planet, scientists in this area will require new approaches and modeling solutions on how carbon footprints can be reduced. AI can quickly learn climate patterns and predict future occurrences. Such prediction will allow professionals to come up with the best course of action to solve emerging issues or prepare in advance for the same.

AI continues to be one of the biggest areas of conversation in tech circles that draws mixed reactions. Although some may view it as the cause of job losses as humans get replaced by machines, others see it as an opportunity to enhance efficiency. Science, for example, is benefiting from AI that has once and again shown its potential to improve results of research and make a better societal impact. It will allow humans researchers to understand and explore how new techniques and materials should be used.