“What do you think you’re doing, Dave?” This iconic line from 2001: A Space Odyssey is spoken by one of the earliest on-screen concepts of artificial intelligence, HAL. In the movie, HAL takes over the spaceship and turns on the crew, making this also one of the earliest on-screen AI villains.

In reality, by 2001, we were still just orbiting the Earth and sending out unmanned probes throughout the solar system. It has been 32 years since the moon landing with no clear picture of how or when we would go back, or beyond. Now in 2020, there is a plan taking shape to not only return to the moon, but to land people on Mars by 2035.

Mars Mission 2035

While the 1969 moon landing was done with computers less powerful than a modern smartwatch, the future of extraterrestrial landings will rely heavily on AI. Evolving for years in spaceflight, the most recent and clear evidence of advancement is SpaceX’s Falcon series of rockets that land on their own. This ability to use AI to continually adjust direction and thrust to land unmanned rockets on both terrestrial and floating landing pads may just be the lynchpin to safely land humans on the surface of Mars.

Previous landings on Mars have included dropping rovers in giant air bags and an engineering feat called the SkyCrane to insert the largest rover onto the surface. Both methods only offer a one-way trip, but if we are going to send people to Mars, we will need a plan to bring them home again. This is where AI-controlled touchdown and lift-off become key.

Traveling Through Space

By relying on AI, propulsion systems can be optimized to carry enough fuel to land and re-launch all while accounting for the right balance of weight required to succeed on the mission. Beyond just controlling propulsion and flight, AI will be critical for the mission to help control other systems as well.

By monitoring and learning the habits and living patterns of the astronauts on the mission, the on-board computer can optimize air handling, temperature, water recycling, and food storage as supply-and-demand of these critical commodities fluctuate throughout the journey.

Living On Mars

Once on Mars, AI will be critical to ensuring a successful mission. Relying on humans to analyze massive amounts of data points on land surveys, seismic data, wind patterns, and analysis of samples would take months, if not years.

The task will be implementing AI that already helps analogous companies here on Earth with similar ventures like locating ideal drilling for oil or identifying flaws for a building site. On Mars, analyses of data will allow the first people on the planet to quickly find the best place to set up their habitat, start taking soil samples, place solar panels, or find a Martian source of water to keep the pioneers sustained without having to rely on the longest supply line in human history.

As we move into the future, 2035 isn’t so far away, and every day, we experience new breakthroughs with AI. By the time the Mars Mission takes shape, it’s conceivable that a real-life version of HAL will launch with the mission, too.

Artificial intelligence (AI) is one of the leading (if not the leading) topics of conversation these days. The recent advances in AI have made it even more than before and are now closer to reality than you might imagine. Until the last decade, computers were not as sophisticated at producing sentences that make sense. They could not compete with humans in many aspects and humans seem irreplaceable in areas such as language. However, the past few years has changed the narrative completely. The field of natural language processing (NLP) has advanced significantly and machines are now competing humans and are even showing signs of understanding human language.

Advances have been enabled by deep learning techniques that can now assess the patterns or word usage and structure from large text. The growing interest of scientists in deep learning has boosted the capabilities of computer speech recognition. Through advancement in deep learning algorithms, error rates can be lowered compared to those made by professional transcriptionists, although intense training of these systems is still required to attain that fully.

Expanding uses

Although the main aim may be academia for now, AI can go above and beyond to other areas. For instance, AI can be useful for consumers who want to adopt it for different purposes. An example of other uses of AI can be in chatbots and voice assistants, both of which are created based on state-of-the-art language models. Chatbots and voice assistants are now used by many institutions in different sectors such finance, healthcare and even government agencies. With a lack of understanding of language, these systems may become ineffective and may slow access to critical services in the industries listed above.

The Winograd Schema Challenge of 2011 was a turning point for AI in relation to natural language processing. The challenge used 273 sets of questions that has pairs of sentences that are the same except for one word that is called a trigger. The NLP system is required to find the difference as a solution to the problem. Such undertakings have seen machine learning and AI coming closer and closer in capabilities to understanding humans, and researchers are predicting that it will not be long before they surpass humans.

Better all the time

For AI to match the capacity of humans, a lot of learning is required. This is what the researchers are currently doing as they are now carefully collecting data from thousands of languages to be used to teach speech recognition systems.  Other developments have ushered in a new era of AI where this technology can now perform much more complex tasks that will not depend on pre-programmed rules to make decisions.

For instance, Robo-advisors such as Betterment among others, are gaining popularity in the finance industry as they can control funds without human input. Professionals in this field believe that AI systems within a few years will become more sophisticated to the extent that no humans will be able to beat them. The question that some people ask is how. Well, with deep learning that takes the concept of AI to the next level by modeling complex non-linear relationships of different layers, a computer can learn from observing data. It uses deep neural networks that is a biologically inspired paradigm that gives the computer the ability to learn like humans.

While it is clear that artificial intelligence has made significant improvements from what it was in the past, a lot needs to be done if it is to be good than humans or even be at the same level. One thing that is for sure is that the road to this success cannot be paved with one discipline. Rather, it requires a collection of techniques, theories and inputs that allow AI to evolve fully.

Corporate giants such as Google, IBM and Apple are competing in the race to acquire private AI companies, but who's making the most investments? RS Components has analysed the acquisition records of some of the world's biggest tech giants over the last decade to see who's investing the most in AI startups. You can view the piece, and each company's acquisitions in full here.

The top 10 companies that have acquired the most AI start-ups since 2009 are:

1. Google, who have bought 30 companies and spent $3,876,340,000 in acquisition costs (based on acquisitions where the prices have been disclosed)

2. Apple, who have bought 18 companies and spent $886,000,000 in acquisition costs in total (based on acquisitions where the prices have been disclosed)

3. Facebook, who have bought 12 companies and spent $1,060,000,000 in acquisition costs in total (based on acquisitions where the prices have been disclosed)

4. Microsoft, who have bought 12 companies and spent $690,000,000 in acquisition costs in total (based on acquisitions where the prices have been disclosed)

5. Amazon, who have bought 9 companies and spent $871,000,000 in acquisition costs in total (based on acquisitions where the prices have been disclosed)

6. Intel, who have bought 6 companies and spent $776,000,000 in acquisition costs in total (based on acquisitions where the prices have been disclosed)

7. Salesforce, who have bought 6 companies and spent $97,799,999 in acquisition costs in total (based on acquisitions where the prices have been disclosed)

8. Twitter, who have bought 5 companies and spent $629,000,000 in acquisition costs in total (based on acquisitions where the prices have been disclosed)

9. AOL, who have bought 4 companies and spent $191,700,000 in acquisition costs in total (based on acquisitions where the prices have been disclosed)

10. IBM, who have bought 4 companies but not disclosed how much money they spent on acquisition costs

Multiple industries are increasingly investing in AI technology - whether to improve their products, make their services more personal or to reduce the amount of time their production process takes. It’s therefore little surprise that this is leading to the AI merger and acquisitions market becoming much broader - and multiple tech corporations, such as Google, Apple and Facebook, are leading the way in this.

As AI continues to become more of a necessity within tech companies’ strategies, it’s clear that they are also recognising the value of acquiring both AI startups and established companies. In the next 10 years, we should expect to see the possibilities around AI advance significantly; whilst the magnitude of what AI has transformed already seems to be beyond any of our expectations, it is still arguably in its infancy. It’s simply no surprise that tech companies are continually seeking to expand their expertise by acquiring these startups.

Although it’s not just companies within the tech industry that are acquiring the most AI startups, they are certainly leading the way. There is also the concern that many companies simply won’t be able to keep up with the likes of Microsoft and Amazon and, at the same time, over the next decade we should expect to see more companies acquiring startups in order to expand their understanding and knowledge of AI.

The likes of Uber should be one to watch, having acquired Mighty AI - who deliver training data to companies that build computer vision models for self-driving vehicles - in order to enhance and scale their self-driving technology. As the industry grows, this may be another group of potential acquirers we could start to see more and more of.

The global race to produce AI has caused some interesting developments in countries around the world. The world’s nations are certainly onboard with the rise of AI. As such, the international community has moved to embrace and employ AI. Major nations around the world have invested in research and development to advance AI.

Invention By Necessity

However, a few countries demonstrate the way specific concerns in one country relate to a specific type of AI development for that nation. We’ve all heard that necessity is the mother of invention, and these countries demonstrate the way their culture influences their development of AI.

Urban AI In China. It shouldn’t come as a surprise that China, a country whose population is just under 1.5 billion, has plans to use AI to optimize urban living. The goal for the use of artificial intelligence in cities is to create a City Brain that can analyze, anticipate, and direct traffic from public transportation and commuters to emergency vehicles.

By reviewing real-time traffic data, Chinese drivers will receive updates that direct them through crowded cities on the best routes. Buses and trains will reroute to avoid congestion with real-time analysis of traffic patterns. By monitoring and detecting city events, Chinese cities can better dispatch emergency vehicles and maneuver them through the streets and traffic lights.

Service AI In Japan. AI innovations in Japan are focused on providing public services to improve social issues. A primary goal for Japan is to use AI to focus on real, not virtual, world problems. Japanese innovators consider AI as an agent for changing human lives, so much of the AI developments are aimed at solving problems that confront humans.

For example, the aging population in Japan experiences problems for which AI can provide solutions. By providing a Human Support Robot to institutions that offer care for the elderly, AI can improve services by automatically collecting and retrieving items required for care, acting like a second pair of hands, eyes, and voice.

Ethical AI In Australia. While Australia hasn’t launched AI products that it wants, there is an understanding of the type of AI that is not wanted. Concerns in Australia relate to the ethical violations from using AI observed in other nations, like automated cameras that mock jaywalkers in real time or facial recognition software that lacks proper programming to account for minority features.

The emergence of AI in Australia is more reactionary, an approach designed to avoid cultural disruptions that developed elsewhere. The push for AI in Australia is to generate technology that fits its environment rather than adopt AI created elsewhere.

All For One, One For All

As countries continue to develop AI that addresses their unique issues, we can expect more innovation to occur. And, within a global community, it’s highly likely that one idea may work for others, or perhaps for all of them. And, if that is the case, then one of these innovations just may change the world.

Artificial intelligence can help seniors feel less lonely, reports BBC.

“I thought at first it was a sign of insanity, speaking to a little thing like that and him talking back!” says 92-year-old John Winward of the first time he tested a smart speaker.

Read the article on BBC

Artificial intelligence keeps the lab humming without human intervention, reports Phys Org. 

An Australian-German collaboration has demonstrated fully-autonomous SPM operation, applying artificial intelligence and deep learning to remove the need for constant human supervision. 

Read the article on Phys Org

According to ZD Net, companies aren’t examining the data that they use for their AI projects carefully enough. 

Data is the raw material that fuels artificial intelligence and machine learning initiatives, but it actually can't be that raw. It needs to be as accurate, timely and well-vetted as possible -- or else AI will deliver erroneous or biased results. 

Read the article on ZD Net

The American National Standard, ANSI/CTA-2089.1, Definitions/Characteristics of Artificial Intelligence in Health Care, defines terms related to AI and associated technologies in health care including "assistive intelligence, synthetic data, remote patient monitoring, and artificial intelligence enabled diagnostic system."

An American National Standard (ANS) is a document that has been sponsored by an ANSI-accredited standards developer (see a list at www.ansi.org/asd), achieved consensus, met ANSI’s Essential Requirements, and been approved by the Institute. The hallmarks of the process ensure that ANS are developed in a manner that is equitable and accessible to all interested stakeholders.

In addition to CTA, a number of activities being advanced by the ANSI Federation are supporting diverse applications of AI, including standards from ANSI members and accredited standards developers such as INCITS and IEEE.

In the international arena, ISO/IEC JTC 1, Information technology – a Joint Technical Committee of the International Organization for Standardization (ISO) and the International Electrotechnical Commission (IEC) – established subcommittee SC 42, Artificial intelligence, in 2018, to focus on the entire AI ecosystem.

Charged with serving as the focal point within ISO/IEC JTC 1 for AI standardization, SC 42 provides guidance to JTC 1 and other non IT-focused ISO and IEC committees as they look to develop AI-related standards in their sector-specific application areas. The committee has 15 projects in development, including efforts on foundational standards (terminology and framework documents for AI and machine learning), trustworthiness (including a risk management approach for AI), and governance implications of AI. CTA is a member of the SC42 U.S. Technical Advisory Group (TAG) administered by INCITS.

ANSI has served as the secretariat to ISO /IEC JTC 1 since its inception in 1987, and provided the chair for the first and only ISO/IEC joint technical committee. The U.S. also holds the secretariat and chair positions of SC 42.

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:

Everything comes with a price and with online business comes an ever-growing customer base. Businesses often fail to provide the right experience to each and every customer. Resulting in negative feedback all across social media and even worse - approximately 86% of buyers will make their buying decisions based on the experience with any business. To avoid these situations Artificial Intelligence came up with a solution - automated chatbots. Chatbots are capable of providing 24/7 customer service without costing you an arm and a leg. In fact, chatbots have been saving a huge sum of money for businesses according to Juniper research, businesses will be saving an estimated $11bn by 2025.

How Chatbots Improve Customer Service?

Customers today, expect an immediate response - more than 80% of your customers would like to have a real-time response to their queries. As per Hubspot, real-time means less than 10 minutes.

So let’s take a realistic look at the number of queries pending in your inboxes; can you provide a real-time response to these queries without automation? Chatbots are your best bet to meet the increasing demand for customer service that is immediate, accurate and “always-on”.

Chatbots 2.0

Not only AI chatbots help you cater to customer queries instantly but they also help you identify actual issues that require human support - the rest of these queries can easily be resolved through self-service options like website help page, FAQs and search results.

As per Forbes, chatbots today are far more advanced, error-free and accurate than they used to be.

Talking About Numbers

• According to salesforce the most popular CRM solution, chatbots have successfully improved customer satisfaction rate by up to 35%.
• The 2016 Gartner research predicted that 80% of all businesses around the globe will opt for chatbots by 2020 - which seems to be nearly accurate as 90% of all customers have rated instant response from business as important or very important in a recent study.
• 90% of all customer queries received by any business in the healthcare, banking and eCommerce industry will be catered by chatbots in 2022 - saving 70 cents per customer interaction. (Juniper Research)
• Not only 67% of your customers enjoy using chatbots all around the world, but actually prefer them over FAQs, search engines and human support agents for various reasons.

Most of the online consumers when asked why would they use chatbots and the answer most commonly found was “to get an immediate response in emergencies”. Next in line was to get a resolution of their issues and complaints. There is a complete study on how consumers use chatbots and how they are transforming customer service and other areas of business.

The Takeaway

Chatbots are smart, instant, cost-effective and user-friendly. A single chatbot is capable of responding to thousands of queries simultaneously and can actually provide a quick resolution to 69% of your customers, who are merely looking for a quick response to their simple questions.

A chatbot integrated into your website and social media can:

1. Improve your customer service response time
2. Boost your brand’s reputation
3. Improve your customer feedback
4. Give you opportunities to sell more to your customers
5. Encourages them to talk about your service to others
6. Review your brand positively on social media and review sites.

AI chatbots are not just a passing trend that the tech industry will get over. They are providing great value to businesses and customers both and will save companies an approximate $8 billion by 2022.

Usama Noman is Founder and CEO of Botsify.  Botsify is a platform for people and businesses to make their own smart Chatbots without knowing how to code.

People themselves know their businesses better than anyone and as experts on business cases they can automate tasks like FAQ's, Customer support, filling up a form, and collecting data from people. 

We aim to change the conversation in Business, Education, and Human Interface with any tech gadget. We automate conversations/ use-cases so that a person can now have a Computer talk/ show/ take instructions from the person across the screen. We are disruptive in our mission to be the best and are bringing new offerings in voice too!

Usama started Botsify working out of his living room stealing hours whenever he could get a chance and made the product from the ground up. During the early days, Usama acted as the sole engineer, designer, business developer, and support for the entire startup. Usama has been a pivotal part of several previous startups and development teams and is the front runner at Botsify. He holds a Bachelor's Degree in Computer Science from FAST NUCES - Karachi, bagging the silver medal and published a paper in IJACS on sentiment analysis on Amazon Reviews along the way.

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.

Despite the many exciting advancements in tech over the last half decade, a concerning narrative is emerging: that machines, rather than being our tools and friend, may come back to bite us. One need only watch an episode of ‘Black Mirror’ to see this moral panic — there is widespread, and not illegitimate, worry that progress will eventually lead to catastrophe.

Certain pieces of academic research only seem to fuel such anxiety. For example, the University of Oxford recently predicted that 47% of the current market will become obsolete over the next 25 years, thereby supporting the suggestion that in the not-too-distant future, we will live mostly workless lives, with robots performing most tasks.

Humanity has its own niche 

However, there is a growing body of evidence to dispel these predictions as simplistic and too negative, and which show that the future relationship between man and machine will actually be peaceful and fruitful. That’s because there are certain aspects of humankind that cannot be replaced by machines.

Firstly, we can’t ignore the fact that technology such as AI will replace some jobs; although others will emerge to replace them. The World Economic Forum takes such a view, predicting that the net number of new jobs as a result of new tech could tally up to the tune of 58 million over the next five years — despite millions of roles being lost. This might mean the mass retraining for individuals worldwide, but not systemic unemployment.

Further, there are some characteristics that are uniquely human. Some might suggest that our ability to empathise will one day be convincingly emulated by machines, but there’s little evidence to support that. And where AI may tend toward the utilitarian and practicable, humans understand moral rules. The example of Microsoft’s Tay bot, which learnt to use discriminatory language and was offensive to minorities on Twitter, serves to illustrate the current limits of this technology.

Some argue that these are the mistakes of still developing technology, but for the foreseeable future at least, humans are still needed to ‘step in’ and correct mistakes.

Amplification of humanity’s abilities

The future of AI will therefore be about its close relationship with humankind, and there are several key aspects to how this partnership will develop. One that requires particular attention is the ‘amplification’ of human traits by machines.

This sounds like jargon, but it refers to a simple idea. A good example arrives from Prospex, a tool created by Fountech Ventures, that allows those in sales to rapidly generate new leads. The tool takes on the mundane, time consuming task of trawling through databases to identify potential leads. Consequently, the human skill (selling a product) is allowed to flourish.

Other AI programs are able to perform similarly useful tasks. For example, designers suffering from creative block can find assistance in a new program from Autodesk, named Dreamcatcher. The program is able to harness creativity by generating new designs based on basic parameters. So, if one needs a new design for a spoon, say, then the input would be ‘includes a handle’ and ‘small shallow bowl’. The designs that are provided can then be sifted through by the designer — and may include the final product design or inspiration for it. Human creativity is still a vital part of the process, but the machine helps amplify it a great deal.

Taking over the boring stuff 

But it’s not just about helping humans work smarter – machines will also take over repetitive tasks that millions of people perform whilst on the job. This may sound like a death knell for repetitive administrative, or factory-based jobs, but that’s simply not the case.

Chatbots, for example, are now used by many large companies for a variety of reasons. They tend to be positioned at the front end of the customer interaction process, and will ask questions based on large datasets, before directing one to the appropriate representative. Once there, the worker at HQ can manage the problem without having to ask repetitive questions all day, and will likely be more fulfilled as a result. The customer, on the other hand, is able to receive the specialised help they need at a faster rate than before.

A recent study conducted by the Harvard Business Review found that in 1,500 companies, firms achieve the most significant improvements when man and machine work in tandem. There is little doubt that the future of tech and AI will make the working lives of millions of people much more enjoyable and efficient. But only when companies receive the necessary support to make the most of the developing tech, and are reassured as to the benefits, will the collaboration between man and machine be realised fully.

Nikolas Kairinos is CEO and founder of Fountech.ai, a company specialising in the development and delivery of artificial intelligence solutions for businesses and organisations.

Would you allow a robot to manage your money? Well, while it may appear as fictional futuristic movie stuff, financial services providers are already doing it. Robots and artificially intelligent computers and systems are now helping in the management of your money and other financial services. According to Accenture, this trend will allow banks to boost their revenues and employment in the next five years. Similarly, AI apps that help people in their Personal Finance Management (PFM) are now available to help individuals in the management of their finances.

It is expected that personal finance management software will have grown tremendously by 2023 globally, with an estimated revenue of $1,213 million. This is a significant growth considering the market was just $837 million in 2017. Although the revenue growth in the AI-driven PFM is a considerable achievement, the rapid increase and development of this industry is crucial in this market.

Automate it

Autonomous finance, which includes financial services driven by AI algorithms to help in decision making on behalf of a customer ,is slowly gathering pace. It has shown the potential of replacing human expertise in financial decision making while also ensuring that customers have access to services such as financial-related coaching and other daily money and planning matters 24/7.

Although this technology is showing good signs already, the question which remains is whether people will trust AI. As evidenced by how people have embraced this technology, the answer to the question is yes. Credit Karma in their research has indicated that the public is comfortable with autonomous finances, even more than they are with the self-driving cars. Millennials who form one of the most significant customer bases have confidence in the use of AI in financial planning since most of them find finances too hard to understand.

Many FinTech companies are already getting into autonomous financing by venturing into different vital niches. Wealth management, for example, is one of the niches that AI is revolutionizing. It uses advanced AI-powered algorithms with big data analytics to offer suggestions on the best course of action to customers. Some known examples are Wealthfront and Betterment both of which are Robo-investing solutions. These two have however been facing competition as new types of fully automated wealth management applications and assistants make entry to the market. For example, Deloitte’s Robo-Advisor 4.0 comes up with optimal asset allocations based on the needs of the customer using a self-learning AI algorithm. It also devises the market conditions in real-time allowing customers to adjust their portfolio based on the prevailing conditions.

Personal savings and budgeting niche is another PFM space that is growing. Examples of apps in this area are YNAB and Mint, which have effective user interfaces that help users access their banking services. Although this first-generation PFM software made a statement a few years ago, new AI-driven solutions are now edging them. Some of these include Digit, Plum and Cleo which were fronted by FinTechs and other financial instruments leveraging on AI to assist in financial coaching and advising on investments, savings and suggest spending. In the lending niche, Chinese fintechs are exploring how they can use AI with digital banking by leveraging big data analytics and machine learning capabilities to ease micro-loans.

As Ai keeps improving, large financial players are in a few years going to resort to AI and autonomous finance to manage your financial needs and services. The goal here is to make it easy for the customer to carry out banking and investment activities easily, automatically and effectively. Since customers want better guidance in matters finances, introducing AI-powered apps allows banks to deliver quality and collect insights which are needed in future decision making.