You may rely on reviews to be a smart consumer and make a good choice for a purchase, but it’s possible that you’re reading an opinion generated by a machine. The use of AI in online reviews searches for and compiles reviews from multiple sources. In addition, AI has also been employed to write reviews, which makes online shopping even more complex than it already was.

Reviews Matter

Since reviews of products or services can make or break a business, they take on great importance in today’s world of consumerism. Enough bad reviews can scare away new and old customers, while conversely, glowing 5-star reviews can attract and drive more sales.

Savvy companies keep a close eye on individual reviews, trends in critiques, and the average star rating for their products. However, with such a prolific selection of online retailers, it can be hard to track all the places a product may be reviewed. More importantly, social media reviews don’t offer a star rating system but rather consist only of text comments and perhaps an emoji or two. For statisticians, that makes the waters very muddy.

The AI Advantage

Searching across all the posts is where AI makes a difference. There are a number of AI-driven services that scour social media platforms and retailers, big and small, for mentions of a product, brand, or company, gather up all of the reviews relevant to the search criteria, and analyze the written reviews to pick out the trends and common themes that may be invisible to someone looking at star ratings or just the company’s customer complaint logs. These systems can go from basic scraping of data for keywords to full analysis of reviews for negative tones, new ways of using a product, or demographics of users that like and dislike a service.

For the past few years, AI has been able to and employed to write reviews. Reviews that lead you to buy a product, go to a restaurant, or enlist a service provider may be written by an AI program. This can allow an unscrupulous supplier to turn on the bot and have it churn out reviews praising their own product on top retailer sites, their own site, and on social media, posing as real users posting believable reviews. While unethical and misleading, this type of behavior would be difficult to track down if done right, and with AI getting better at impersonating human typed speech every day, may not even set off alarm bells to have anyone look into the behavior.

On the flip-side of this nefarious use of AI, companies can enlist an AI reviewer to tirelessly spit out convincing reviews of competitive products, highlighting shortcomings, poor quality, lack of service, or anything that will drive sales down. The target company may respond to these reviews, show consumer good will, and try to reach out to smooth the waters, but a string of 1 and 2 star reviews may create enough damage to spell doom for a product or company.

How To Use Reviews

As AI further infiltrates online consumer reviews, it will be up to the consumer to decide which reviews to trust and which to question. Users can no longer believe everything they read about a product or service, but it’s a good start. Look for common denominators between the reviews, and while you might not spot the computerized versions, you could still use reviews to inform your choices.

Artificial Intelligence (AI) has achieved a lot since it came into existence. However, the main objective has always been to get high-level AI systems that mimic humans in most of the things they do. In essence, the primary objective of AI systems is to function independently, like humans. However, that AI is conscious is no longer a matter of if but when. This was envisioned by Norbert Wiener, the renowned father of cybernetics in his book The Human Use of Human Beings, back in 1950. Although Norbert lived at a time when vacuum tubes were the building blocks of computers that were few at a time, he foresaw how automation would change the society for the better.

Unlike other researchers who see AI as a technology that will imitate and replace humans, Norbert noted that this technology would change humans in many ways, including creative activities. Comparing the technology with a whirlpool, he noted that we would not abide by what humans have been doing for ages, but we will instead conform to the new changes.

Here we are in 2020 where artificial intelligence is the talk of the day. With the focus increasing on the potential use and misuse of this technology, the question that many people are now asking is whether AI can ever be considered to be fully conscious or not. The majority of experts believe that we are decades away from seeing AI emerging as conscious. Although many notable achievements have been made in its research, no one fully understands what conscious AI looks like. As deep learning continues advancing, many researchers now believe that the time for machines to think like humans is not so far like it was in the 1950s or even 2000s. Others, however, believe that we will need to do more with neural networks and algorithms, while others still believe that machines will never match humans at all.

Although many scientists may have a better understanding of the AI systems, they have little knowledge of intelligence in relation to the human brain or consciousness. This means that we do not fully understand what intelligence is when it comes to humans, that can make it easy to compare human to AI systems. Going by this, what we know is some neural networks and algorithms but little understanding of whether AI has or will ever reach the level of human intelligence. It is also hard to demonstrate a path towards AI consciousness, although it is not impossible.

According to Johannes Kleiner and Sean Tull, AI might already be conscious. This is if their research paper on the nature of consciousness is anything to go by. According to their research, the duo notes that the universe and anything in it have some form of consciousness. They go ahead to note that anything that exists in the universe exhibits some characteristics of some degree of consciousness. Their argument suggests that consciousness emerges as a result of physical states and that human consciousness is due to the ability to experience things. For instance, a tree is conscious because it has the ability to sense sunlight and may even bend towards it. Since a tree is a living thing, it is hard to draw a comparison between them with the AI systems.

While many theories are already there on consciousness, it is hard to point out precisely whether AI is already conscious or we have to wait longer before AI can mimic humans not only in their actions but in thinking as well. One thing we can all agree on despite varying opinions and views is that AI has advanced to the extent that it can now compete with humans in many areas. More is expected in the coming years as new inventions keep coming.

The history of AI dates back to about 1955 and the first AI device, “The Logic Theorist.” It’s not a new concept. There have been ideas about the potential for AI and the ability for AI to revolutionize our lives for a long, long time. However, it’s only been recently that the practical applications and developments have begun to snowball.

The Tipping Point

One of the most important keys to unlocking this rapid growth of AI happened on August 6, 1991; the Internet came to life. AI began to proliferate in a ‘snowball effect’ of development and implementation.

Computers and users became connected, establishing the hive mind on which AI could scaffold. This meant that AI programs could begin to connect as well, merging power and programming. Since AI relies on data to make and refine decisions, the internet gave rise to ever more data for better, faster, and more consistent data mining and AI applications.

Gathering Steam

At first, AI was found mostly in developmental circles and labs. Experiments with programs that could learn and machines that could perform a function ran the gamut from robots to voice activated devices. The ideas were novel but not necessarily practical.

Then, businesses learned the value of applying AI for data management and analysis. Supply chain, manufacturing, marketing and just about any industry relying on a database saw the benefits of machine learning to mine data.

However, the real momentum built when cell phones employed an AI program as a voice activated assistant, placing AI quite literally into the hands of users all over the world. AI became a mainstream concept as well as an ever improving and accessible tech.

Cooking With Gas

Now, AI was in demand, and supply rushed to fulfill the need. Devices that used AI or integrated with the cell phone to allow voice commands burst onto the scene. Home AI assistants, cars with internet and AI programs in charge of safety and entertainment features, and smart home appliances from faucets that filled on voice command to toilets that recognize the user enchanted consumers the world over.

The ability to integrate devices and to access the internet through fast WiFi and 4g allowed for exponential growth in speed. Just one cell phone could control the lights, temperature, and appliances at home, park the car, and pay the bills.

In the corporate realm, artificial intelligence was improving the speed of companies’ deliveries, manufacturing, advertising, quality, and services. Accuracy and efficiency of AI compared to human oversight allowed faster growth and more product developments.

Barreling Down The Hill

Speed, accessibility, and practice are happening in the world of AI at an incredible rate. The science lab, consumer users, and business applications have all been using more and better AI. Whereas augmenting human efforts was the initial use for AI, today’s AI is on the brink of automation, fully taking on the labor previously belonging only to humans.

As we see the world embrace and apply AI programs and devices, it’s pure physics as work: the snowball will only continue to get bigger and bigger and the pace will move faster and faster.

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.

Signals Analytics, the next generation advanced analytics platform that leverages external data to uncover trends and predictive insights, today announces a new rollout of its award-winning platform, leveraging breakthroughs in NLP, machine learning and other capabilities that enhance the precision, detail and utilization of advanced analytics in the enterprise. New features that span the entire data journey such as e-commerce product clustering, author, affiliation, and brand refinement, data mart integration and a daily alert allow businesses to seamlessly integrate Signals Analytics into their existing business intelligence technology stacks, while surfacing highly-detailed and predictive actionable intelligence across a broader range of use cases. 

While it is well-established that data-driven organizations are significantly more successful than their peers, many organizations express difficulty with actually standing up analytics and incorporating data-driven decisioning processes in their enterprises. The challenge becomes even more acute as the interest in connecting external data grows to capture consumer sentiment and a better understanding of the market landscape. The issue that is hardest to overcome is the intrinsic nature of these external data sources - they are generally unstructured, unconnected and made up of many different data types that can change often. The latest advancements announced today will allow companies to seamlessly combat these common obstacles so they can better leverage data and analytics in their business processes.

“There is a clear distinction between market leaders and laggards with their use of analytics, and at times like these the value of data and the utilization of data will only help to further separate winners from losers,” says renowned author and analytics expert, Tom Davenport. “While the benefit of analytics is well understood in theory, in practice, implementing analytics is very difficult and dealing with external data makes it even more challenging. Platforms like Signals Analytics put the end goal within reach in a practical, accessible way, helping to address many critical business questions across the enterprise.”

Recipient of the Frost and Sullivan 2020 North America Enabling Technology Leadership Award, Signals Analytics encompasses main components of a data fabric: the data collection layer, the data classification layer, and the data access layer that are all configurable to meet the business needs. More than 13,000 data sources get connected and harmonized by the analytic engine, which extracts context and sentiment and generates insights that are presented in more than 100 business-ready analytic models and apps. Because the insights generated by the platform are extremely accurate and contextualized, they help to shorten product development lifecycles, reduce the timelines associated with market intelligence and competitive positioning, and maximize the effectiveness of business decisions and marketing strategies, all of which brings brands closer to their customers, giving them first mover advantage in predicting and reacting to trends. With an impressive roster of marquee FMCG and pharmaceutical brands, Signals Analytics has already proven to deliver maximum returns on investment. The new capabilities take the platform to a new level of accuracy, scalability and insight.

“From day one, our commitment has been to use AI to power the future of market intelligence and increase the business impact of analytics for the enterprise,” said Signals Analytics CEO and Co-Founder, Gil Sadeh. “What this means, given the volume of external data and how fast it changes, is a continued focus on connecting more and more data sources, pushing the envelope with NLP to extract granular and actionable insights, and enabling integrations with other business intelligence platforms. The capabilities we are announcing today, such as auto ML and product clustering for example, showcase the depth of our AI-focus, while the daily alerts demonstrate how our platform delivers strategic and tactical value to multiple stakeholders across the enterprise.”

New Advanced Analytics Capabilities

• New NLP and automatic machine learning (auto ML) engines improve data collection and classification and help address unique external data analytic challenges, such as product clustering, author, affiliation, and brand refinement.

Product clustering is a capability that identifies products that are the same but have different names across different e-tailers, preventing skewed analytic results. With greater than 90% recognition accuracy, organizations can configure this capability and tailor predictive analytics outcomes to align with their business. This means more prescient decision-making that lowers risk and maximizes business outcomes.

Author, affiliation and brand refinement capabilities are part of a set of new named entity recognition (NER) features that allow accurate representation models for precisely identifying authors, research institutions and FMCG brands within a large depository. The model compares the authors and brands of each new research paper, patent, or clinical trial, and determines if there is a match or if the representation model should be augmented.

• Expanded openness of the platform through the new Data Mart integration offers a direct connection to Signals Analytics’ connected and classified data sets for organizations to conduct analytics in their own business intelligence environments.

“With today’s announcement, we are answering a call from the market for more open platforms that increase the impact of analytics across the enterprise, with greater flexibility to support multiple use cases while reducing the reliance on in-house analytic, data science and machine learning expertise.  Extending the connectivity and openness of the platform helps our customers move away from cumbersome analytics projects, while still fulfilling their need to be data-driven and precise in their decision-making process,” Sadeh concluded.

More information is available on the “Competing with Analytics and External Data” on-demand webinar, co-hosted by Tom Davenport and Gil Sadeh.

According to Futurism, a dataset that is used to train AI was littered with racial and misogynist slurs. 

A machine learning algorithm is only as good as the data it’s trained on. Unfortunately, a massive and popular training dataset from MIT taught a bunch of algorithms to use racist and misogynistic slurs.

Read the article on Futurism

An artist is using artificial intelligence to complete true to life portraits on people in history, reports the Daily Mail. 

A Dutch artist is using modern technology to create realistic photo-style portraits of famous figures only depicted in paint and sculpture. 

Read the article on Daily Mail

According to Business Insider, artificial intelligence the fashion industry is hoping that artificial intelligence will help the fashion supply chain smooth out the ups and downs within the industry. 

Even before the coronavirus pandemic crashed the global economy, disrupting supply chains and stalling consumer demand, the fashion industry was not looking forward to walking the ramp in 2020. 

Read the article on Business Insider

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:

 Bias is one of the biggest documented weaknesses of humans. This can be seen from implicit tests that show that we are not even aware of some of our biases. Although we may say that these biases are part of human nature, experiments have shown that they can affect our judgments and outcomes. Over the past few years, there has been an argument on whether human biases can be solved by artificial intelligence or whether these biases can make their way into AI systems and cause harm. This comes at a time when AI is being deployed by many companies across different industries. The industries need to be aware of the risks of biases ad how AI can be affected by it.

All AI models are developed by human beings and can, without a doubt, have some form of bias. For instance, machine learning algorithms have biases that are a reflection of organizational teams, designer teams, and data scientists. They are also vulnerable to the prejudices of data engineers who gather data. Just like the lack of fairness that we have been experiencing from human decision-makers in different areas, AI is expected to also suffer from it as these prejudices can find their way into algorithms in a variety of ways.

AI systems learn to make decisions based on training data that can be as a result of human prejudice. As such, decisions that these systems can arrive at may sometimes be biased. They can be seen in areas such as gender, race, and sexual orientation, among others. Amazon, for instance, stopped using its hiring algorithms after it emerged that it favored applicants based on specific words such as “execute” or “captured” that were common in men’s resumes. Another major flaw in AI systems that results in bias is data sampling whereby groups are over or underrepresented in the training data. This results in specific groups, such as minorities and women suffering.

Despite the possibility of bias, AI will have some level of trustworthiness, which is a backbone of machine learning. However, even a trustworthy model will have some form of weakness that must be looked into if fair systems are to ve developed. It is upon humans to ensure that training data is fair and representative by all standards. To achieve this and ensure optimal results, organizations must have tech teams with diverse members. These members should be in charge of building AI models and creating training data to be used by AI systems.  Furthermore, organizations should try to find out a highly comprehensive data and experiment it with different metrics and datasets.

As machine learning and artificial intelligence systems increasingly become complex, it has become critical now than ever to have training data that is annotated by humans in an unbiased manner. During the training of machine learning models, human bias can damage the accuracy of these models. If there is are in-house teams that are involved in interpreting training data, they should adhere to an unbiased approach in the classification of data. This may involve using different approaches and styles that are diverse.

Without a diverse approach in classifying data, there is a high risk of less accurate models being created. On the other hand, the external partners who are involved in the collection or processing of data should ensure the job is done by diverse crowds to ensure data is representative. The data annotation design tasks should be carried out carefully. Once the training data has been created, it should be checked to identify whether data has any bias or not.

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.