Artificial Intelligence

Fear of the Unknown: Artificial Intelligence

Artificial Intelligence (AI) will be the most popular and developed technological trend in 2020 with a market value projected to reach $70 billion.

AI is impacting several areas of knowledge and business, from the entertainment sector to the medical field where AI is utilizing high-precision algorithms through machine learning that can produce more accurate diagnoses and detect symptoms of serious diseases at a much earlier stage.

The innovation that AI offers to industry, businesses, and consumers is positively changing all processes. The new decade will be driven by the rise of automation and AI-induced robotics.

However, there is a huge exaggeration and hysteria about the future of Artificial Intelligence and how humans will need to adapt and get used to living with it. In fact, AI is a topic that has polarized popular opinion. What is true is that AI will become the core of everything that humans interact within the coming years, and beyond. Hence, to have a clear opinion about AI and its impact, it is important to understand what it is and what are the types of artificial intelligence that exist.

General Artificial Intelligence (AGI) is the type of AI that can perform any cognitive function in the way a human does. The technology is not there yet but it is developing at a fast pace and there are interesting AI projects such as Elon Musk’s Neuralink. 

Today, narrow AI applications, intended to develop only one task, such as IBM Watson, Siri, Alexa, Cortana, and others are the ones that share the world with us. The key difference between the AGI or wide artificial intelligence and the narrow or weak AI is the goal setting and the volition.

In the future, AGI will have the ability to reflect on its own objectives and decide whether to adjust them or not and to what extent. We have to admit that, if done right, this extraordinary technological achievement will change humanity forever.

However, there is still a long way to go to get to that point. Despite this, many fear that Super Artificial Intelligence (ASI) will one day go beyond human cognition, also known as the technological singularity.

At the moment, in society, there are two emerging and visible groups: on the one hand, the public is informed- in this group, trust towards new and emerging technologies has been increasing over time. On the other hand, there is the mass population -a group where trust remains stagnant.

Of course, social networks also play a role here. It’s not just about consumption, but about amplification, with people who share news more than ever and discuss issues relevant to them. Confidence used to be from top to bottom, but now it is established horizontally from equal to equal.

Will AI benefit or destroy society?

AI can only become what humans want it to become. Humans have the task of coding their AI creations. If the mass population is increasingly anxious about AI, this is due to fear of the unknown. Perhaps it is also because there is very little information available about the benefits AI offers to balance with those who believe that AI will destroy society and take away their jobs.

For now, AI has only been providing great benefits and its coverage in the medium term can only benefit and optimize many areas of human activity.  

Machine Learning

The Future of the Stock Market: Machine Learning-based Predictions

Since the arrival of automated investment and artificial intelligence in the stock markets, the search for the Holy Grail of stock market investment has been to develop and refine the algorithm that would allow for predicting the behavior of the stock market and the actions of the companies in the future listed.

Needless to say, knowing how to predict the future trends of stocks translates into cash and sound money, and it is also necessary to act based on those predictions ahead of other investors, before the scenario is discounted by all in the market. And now there is a new generation of Machine Learning (ML) that yields a success rate in the future that cannot be the result of mere chance: yes, ML already hits a very high percentage, and also with a success rate much larger than the vast majority of human stock advisors, 79% and even 90% in certain cases.

In the stock market, first having always meant earning more money or losing less. Being the first to negotiate literally translates into money. It is taking it for granted that “information is power”, and operating with an anticipatory vision where others are disoriented and giving “blind sticks” in the markets. It goes without saying that in the second it is usually them who end up losing the sticks of losses because there is nothing worse in the bags than having no more strategy than a few misleading hunches. In this, an automated investment may be contributing a lot to the markets, since it establishes clear and synthetic investment rules, and avoids the scenario of breaking them down by human passions that are very dangerous for your pockets, such as euphoria or panic.

But even with an AI that will obviously be marketed, the more massively the better, it is highly likely that those predictions in the future will be available to many investors -human or synthetic. And under this scenario, when many in the market have that prediction with a high probability of being fulfilled, again it must be said that being the first to negotiate will result in money again, with the addition that now the speed will be absolutely decisive to shed profits or losses on each operation.

Only technical analysis is used as a tool for stock predictions because it is considered to be easy-going for the algorithm to learn and the human to interpret, giving predictions where there is only one attribute i.e., historic prices of stock. The current algorithm gives predictions of one single stock that is given as input to find future predictions.

Here are a few companies that use Machine Learning in Technical analysis for stock prediction:

● Trading Technologies.
● GreenKey Technologies.
● Kavout.
● Auquan.
● Epoque.
● Sigmoidal.
● Equbot.
● AITrading.

Recently an Israel based stock forecast company named ‘I Know First’ using predictive Artificial Intelligence demonstrated an accuracy of up to 97% in its predictions for S&P 500 and Nasdaq indices, as well as their respective ETFs. So there’s a lot that can be achieved or explored with the use of Machine Learning in stock prediction.AI is just a new twist to what has already been the virtualization of markets since the arrival of automated investment.

As we said before, this profitable telecommunications-operational symbiosis is not exactly something new as it has been that way since the dawn of automated investment some five years ago. It is true that it began by taking no significant benefits from the small (even imperceptible) market fluctuations, in which the agility of the operation was fundamental since these spikes in share prices can last even for a fraction of a second. If one was able to operate in the same order of temporal magnitude, there was a possible benefit to be taken out of the market. But what is really news now is that, as we will analyze it, this ultra-rapid factor in the operation acquires double relevance under the scenario of the existence of a successful AI algorithm.

We must emphasize that these algorithms may be contributing to improve price formation and to make the market work better, but the negative side is to delegate human decision-making capacity to algorithms that know how they will react to black swan events. Indeed, we said before that human error is being carried away by euphoria or panic, but we said this assuming regular conditions. In scenarios of volatility not suitable for cardiac and black swans, although many investors can continue to fall prey to those unprofitable passions, there is the moment when the value of a mature, professional, and experienced manager is literally worth in gold, being the moment when he should take the helm.

It is necessary to consider as a requirement of the software architecture of the investment programs that something like the automatic pilot of an airplane is implemented: in regular conditions, the aircraft is perfectly piloted by the automated system, but when things look rough, the pilot can regain control of the ship and get passengers out of vital trouble. The automated investment must take these same precautions because today, the human mind is still infinitely more intuitive and analytical than an algorithm that after all is based on historical data which may sometimes not serve as a seed to iterate the learning of artificial intelligence. This can be especially so when we must weigh factors of subjective perception, which can also have a strong influence on the market, and whose subjective complexity is a great degree of added difficulty for an objective robot, not to mention the global cost of continually training with recurring iterations a multitude of investment robots across the planet.

Bid farewell to the simple real-time investment that was new in the 90s and welcome the era of the real-time market investments. We will operate based on an ephemeral ever-changing market scenario, which will cease to exist as soon as we do it together with a certain critical
mass of investors. Never before have your investments had more aggregate capacity to cause disruptions in the market. Welcome to the new reality.

This article is co-authored by Dr. Raul Villamarin Rodriguez and Rajat Toshniwal, Woxsen School of Business

Machine Learning

The Future of HR from 2020: Machine Learning & Deep Learning

The future of HR lies in Deep Learning which is steroid machine learning. It uses a technique that gives machines an improved ability to find, and amplify, even the smallest patterns. This technique is called a deep neural network: deep because it has many layers of simple computational nodes that work together to search for data and deliver a final result in the form of prediction.

Neural networks were vaguely inspired by the inner workings of the human brain. The nodes are like neurons and the network is like the brain itself. But Hinton published his breakthrough at a time when neural networks had gone out of style. No one really knew how to train them, so they were not giving good results. The technique took almost 30 years to recover. But suddenly, it emerged from the abyss.

One last thing we should know in this introduction: machine learning (and deep) comes in three packages: supervised, unsupervised and reinforced.

In supervised learning, the most frequent, the data is labeled to indicate to the machine exactly what patterns to look for. Think of it as something like a tracking dog that will chase the targets once you know the wrapper you’re looking for. That’s what you are doing when you press play on a Netflix program: you are telling the algorithm to find similar programs.

In unsupervised learning, the data has no tags. The machine only searches for any pattern it can find. This is like letting a person check tons of different objects and classify them into groups with similar wrappers. Unsupervised techniques are not as popular because they have less obvious applications but curiously, they have gained strength in cybersecurity.

Finally, we have reinforcement learning, the last frontier of machine learning. A reinforcement algorithm learns by trial and error to achieve a clear objective. He tries many different things and is rewarded or penalized depending on whether his behaviors help or prevent him from reaching his goal. This is like when a child behaves well with a praise and affection. Reinforcement learning is the basis of Google’s AlphaGo, the program that surpasses the best human players in the complex Go game.

Applied to Human Resources, although the growth potential is wide, the current use of Machine Learning is limited and presents a dilemma that must be resolved in the future, related to the ability of machines to discover talent in human beings, beyond their hard and verifiable competencies, such as level of education, etc.

Software intelligence is transforming human resources. At the moment it has its main focus on recruitment processes, which in most cases is a very expensive and inefficient process where our goal is to find the best candidates among thousands of them, although we can find multiple application examples.

A first example would be the development of technology that would allow people to create job descriptions that are gender-neutral to attract the best possible candidates, whether male or female. This would boost a group of job seekers and a more balanced population of employees.

A second example is the training recommendations that employees could receive. On many occasions these employees have many training options, but often they cannot find what is most relevant to them; Therefore, these algorithms present the internal and external courses that best suit the employee’s development objectives based on many variables, including the skills that the employee intends to develop and the courses taken by other employees with similar professional objectives.

A third example will be Sentiment Analysis, which is a form of NLP (Natural Language Processing) that analyzes the social conversations that are generated on the Internet to identify opinions and extract the emotions (positive, negative or neutral) that these implicitly carry. With the sentiment analysis it is determined:

-Who is the subject of the opinion.

-About what is being said.

-How is the opinion: positive, negative or neutral.

This tool can be applied to words and expressions, as well as phrases, paragraphs and documents that we find in social networks, blogs, forums or review pages. The sentiment analysis will determine the hidden connotation behind the information that is subjective.

There are different systems of sentiment analysis:

-Analysis of feeling by polarity: Opinions are classified as very positive, positive, neutral, negative or very negative. This type of analysis is very Simple with reviews made with scoring mechanisms from 1 to 5, where number 1 is very negative and 5 is very positive.

-Analysis of feeling by type of emotion: The analysis detects emotions and specific feelings: happiness, sadness, anger, frustration, etc. For this, there is usually a list of words and the feelings with which they are usually related.

-Sentiment analysis by intention: This system interprets the comments according to the intention behind: Is it a complaint? A question? A request?

A fourth example is the Employee Attrittion through which we can predict which employees will remain in the company and which will not be based on several parameters as shown in the following example-

A screenshot of a cell phone

Description automatically generated
Source: IBM (IBM Watson sample dataset)

These four cases are clear examples in which Machine Learning elevates the role of human resources from tactical processes to strategic processes. Smart software is enabling the mechanics of workforce management, such as creating job applications, recommending courses or predicting which employees are more likely to leave the company, giving the possibility to react in time and apply corrective policies for those deficiencies.

From the business point of view, machine learning technology is an opportunity to drive greater efficiency and better efficiency in decision making. This will help everyone to make better decisions and, equally important, will give Human Resources a strategic and valuable voice at the executive level.

Prof Raul Villamarin Rodriguez

Artificial Intelligence


Artificial intelligence is not new, yet there have been quick advances in the field as of late. This has to a limited extent been empowered by improvements in processing power and the colossal volumes of advanced information that are presently produced. A wide scope of utilizations of AI are currently being investigated with significant open and private speculation and premium. The UK Government reported its aspiration to make the UK a world head in AI and information advancements in its 2017 Industrial Strategy. In April 2018, a £1bn AI part bargain between UK Government and industry was reported, including £300 million towards AI research. AI is commended as having the capacity to help address significant wellbeing challenges, for example, meeting the consideration needs of a maturing populace. Significant innovation organizations – including Google, Microsoft, and IBM – are putting resources into the improvement of AI for human services and research. The quantity of AI new businesses has likewise been consistently increasing. There are a few UK based organizations, some of which have been set up as a team with UK colleges and clinics. Organizations have been framed between NHS suppliers and AI engineers, for example, IBM, DeepMind, Babylon Health, and Ultromics.

Healthcare Organization – Artificial intelligence can possibly be utilized in arranging and asset assignment in wellbeing and social consideration administrations. For instance, the IBM Watson Care Manager framework is being guided by Harrow Council with the point of improving cost productivity. It matches people with a consideration supplier that addresses their issues, inside their distributed consideration spending plan. It additionally structures singular consideration plans and claims to offer bits of knowledge for increasingly successful utilization of care the executive’s resources. AI is likewise being utilized with the point of improving patient experience. Birch Hey Children’s Hospital in Liverpool is working with IBM Watson to make a ‘psychological medical clinic’, which will incorporate an application to encourage collaborations with patients. The application means to distinguish persistent tensions before a visit, give data on request, and furnish clinicians with data to assist them with delivering suitable medications.

Medical Research – Artificial intelligence can be utilized to dissect and distinguish designs in enormous and complex datasets quicker and more decisively than has recently been possible. It can likewise be utilized to look the logical writing for pertinent investigations, and to consolidate various types of information; for instance, to help sedate discovery. The Institute of Cancer Research’s jars AR database joins hereditary and clinical information from patients with data from logical research and uses AI to make forecasts about new focuses for malignancy drugs. Researchers have built up an AI ‘robot researcher’ called Eve which is intended to make the procedure of medication disclosure quicker and more economical. (K.Williams, 2015) AI frameworks utilized in human services could likewise be significant for restorative research by coordinating reasonable patients to clinical examinations.

Clinical Care – Artificial intelligence can possibly help the analysis of illness and is presently being trialed for this reason in some UK emergency clinics. Utilizing AI to investigate clinical information, examine distributions, and expert rules could likewise advise choices about treatment


APPLICATIONS – A few applications that utilization AI to offer customized wellbeing appraisals and home consideration exhortation are as of now available. The application Ada Health Companion utilizes AI to work a talk bot, which joins data about side effects from the client with other data to offer conceivable diagnoses. GP at Hand, a comparative application created by Babylon Health, is as of now being trialed by a gathering of NHS medical procedures in London. Information devices or visit bots driven by AI are being utilized to help with the administration of constant ailments. For instance, the Arthritis Virtual Assistant created by IBM for Arthritis Research UK is learning through associations with patients to give customized data and guidance concerning prescriptions, diet, and exercise. (Release, 2017) Government-financed and business activities are investigating manners by which AI could be utilized to control mechanical frameworks and applications to help individuals living at home with conditions, for example, beginning time dementia. Man-made intelligence applications that screen and bolster tolerant adherence to recommended drug and treatment have been trialed with promising outcomes, for instance, in patients with tuberculosis. (L.Shafner, 2017) Other apparatuses, for example, Sentrian, use AI to examine data gathered by sensors worn by patients at home. The point is to identify indications of decay to empower early mediation and avoid medical clinic affirmations.

PUBLIC HEALTH – Artificial intelligence can possibly be utilized to help early location of irresistible malady flare-ups and wellsprings of pandemics, for example, water contamination. (B.Jacobsmeyer, 2012) AI has likewise been utilized to anticipate unfavourable medication responses, which are assessed to cause up to 6.5 percent of emergency clinic affirmations in the UK.

Babylon a UK fire up plans to “put an open and reasonable wellbeing administration in the hands of each individual on earth” by putting man-made brainpower (AI) apparatuses to work. Right now, the organization has activities in the UK and Rwanda and plans to extend to the Middle East, the United States, and China. The organization’s technique is to consolidate the intensity of AI with the medicinal aptitude of people to convey unrivalled access to human services.

How does Babylon’s AI work?

A submitted group of research researchers, architects, specialists and disease transmission experts are cooperating to create and enhance Babylon’s AI capacities. A great part of the collaboration is on the advancement of bleeding edge AI explore; this is being passed through access to enormous volumes of information from the therapeutic network, constant gaining from our very own clients and through input from Babylon’s very own specialists.

The knowledge graph and user graph:

Babylon’s Knowledge Graph is one of the biggest organized medicinal information bases on the planet. It catches human information on present day medication and is encoded for machines. We utilize this as the reason for Babylon’s clever parts to address one another. The Knowledge Graph monitors the significance behind therapeutic phrasing crosswise over various restorative frameworks and various dialects. While the Knowledge Graph gives the general information about medication, tolerant cases are kept in the User Graph. Conjunction of the Babylon Knowledge Graph and the User Graph takes into consideration more revelation. We can coordinate indications with data and results, continually improving the data we give.

The inference engine:

Essentially seeing how clients express their indications and hazard factors isn’t enough to give data on perhaps coordinating conditions. At the core of Babylon’s AI is our surmising motor, an amazing arrangement of AI frameworks, equipped for thinking on a space of >100s of billions of blends of indications, illnesses and hazard factors, every second, to help distinguish conditions which may coordinate the data entered. The surmising motor gives our AI the capacity to give thinking productively, at scale, to carry wellbeing data to millions.

Natural Language Processing (NLP):

Our AI can’t give data to patients on the off chance that it can’t get them, and patients won’t utilize our AI if they can’t get it. To help cross over any barrier, we utilize Natural Language Processing (NLP). NLP enables PCs to translate, comprehend, and afterward utilize each day human language and language designs. It separates both discourse and content into shorter parts and deciphers these increasingly reasonable squares to comprehend what every individual segment means and how it adds to the general importance, connecting the event of restorative terms to our Knowledge Graph. Through NLP our AI can decipher counsels, outline clinical records and visit with clients in a progressively characteristic, human way.

Machine Learning research at Babylon:

All through the Babylon stage we use Machine Learning (ML) for an assortment of undertakings. In the induction motor we consolidate probabilistic models with profound learning methods to accelerate the deduction procedure. In the Knowledge Graph we anticipate new connections between medicinal ideas dependent on perusing restorative writing. In NLP we assemble language understanding models dependent on enormous scale datasets of communications with our clients and information from the web. We use ML to show our NLP framework new dialects.

Babylon would not be achievable without the utilization of cutting-edge ML procedures, so we’ve put fundamentally into building a world class inquire about group in this field. Babylon is additionally quick to contribute back to the AI people group through papers, blog entries, and by publicly releasing a portion of our work to help all.

Services Babylon Offers:

Babylon engineers, doctors, and researchers built up an AI framework that can get information about the manifestations somebody is experiencing, contrast the data with a database of known conditions and sicknesses to discover potential matches, and afterward recognize a game-plan and related hazard factors. Individuals can utilize the “Ask Babylon” highlight to ask about their restorative worries to get an underlying comprehension of what they may be managing, yet this administration isn’t proposed to supplant the mastery of a specialist or be utilized in a health-related crisis.

In quest for its strategic, offers a “converse with a specialist” administration by means of its application, GP at Hand that gives day in and day out access to medicinal services experts through video or sound conferencing. The application can be downloaded from Google Play or the App Store. At the conference, specialists can offer medicinal guidance, answer questions, examine treatment, and can arrange solutions that can be conveyed to a patient’s entryway. All the patient’s clinical records are put away in a safe domain, and their wellbeing history can be gotten to and referenced when it’s required. If a patient needs to return to their arrangement, they can audit the restorative notes and replay an account of the arrangement whenever would not be achievable without the utilization of cutting-edge ML procedures, so we’ve put fundamentally into building a world class inquire about group in this field. Babylon is additionally quick to contribute back to the AI people group through papers, blog entries, and by publicly releasing a portion of our work to help all.

Another feature that is available on the app is Healthcheck. Built with the support of doctors, scientists and disease experts, this AI tool can take answers from questions about family history and a person’s lifestyle and compare it to the medical database to then create a health report and insights to help someone stay healthy.

The beginning up claims that in its own tests, the AI framework was spot on80 percent of the time and that the instrument was never intended to totally supplant the counsel of a genuine specialist, yet to decrease holding up times and to assist specialists with settling on progressively exact choices. The world is confronting an outrageous lack of specialists and medicinal experts, and tech, for example, what Babylon offers is one approach to help improve the social insurance of a great many individuals. As indicated by NHS England, “Every security case [of Babylon] satisfies the guidelines required by NHS and has been finished utilizing a hearty appraisal technique to an elevated expectation.”

While it probably won’t be an ideal framework, Babylon shows that man-made reasoning has sufficiently advanced to work nearby medicinal services experts and can be a useful instrument. Be that as it may, patients still need to stay to be their very own furious social insurance advocates. On the off chance that the guidance got from man-made reasoning doesn’t appear to hit the imprint, it’s a word of wisdom to demand a subsequent supposition—from a human.

AI for the patient and provider

Babylon Health needs everybody with a cell phone to approach moderate medicinal services. They accept an application that offers moment conclusion is the key. As their CEO, Ali Parsa, disclosed to the Telegraph: “[Medical professionals] are the costliest piece of medicinal services. What’s more, the second… is timing… [By] the time [most diseases] present their indications a £10 issue has become a £1,000 arrangement.”

Babylon Health accepts they can drop both of those expenses. Today, Babylon Health offers a free application that makes it basic for clients to follow their wellbeing and counsel their AI-controlled chatbot. For a charge, clients can video-visit with top specialists who can get to that client’s wellbeing records and a lot of exclusive AI-fuelled apparatuses that Babylon Health cases can improve treatment quality. By following the vitals, medicines, and results over an expansive client base, Babylon wellbeing has tapped an unbelievably important dataset. This dataset makes it adaptable to consistently improve their AI’s presentation nearby clients’ wellbeing.

IBM Watson for Oncology has a smaller center: improving the results of disease medications. IBM accepts they can give each restorative expert treating disease a similar knowledge that specialists at top malignancy examine focuses have. IBM has banded together with experts at Memorial Sloan Kettering to prepare their PCs with an abundance of restorative records and research. Propelled in 2016, Watson underpins specialists with tolerant explicit suggestions from bleeding edge medications in a small amount of the time. As indicated by Deborah DiSanzo, the General Manager of IBM Watson Health, Watson for Oncology had just been utilized in the treatment of 16,000 patients by the second from last quarter of 2017. With PCs taking care of the examination, specialists can concentrate on what people exceed expectations at: treating the passionate misery of a patient battling malignant growth.

Data for artificial intelligence is food for thought:

Both IBM Watson and Babylon Health concur: specialists can convey better treatment by gaining from the aftereffects of different patients. Computer based intelligence can gain from chronicled information and figure how a patient’s sickness would react to treatment choices. The two organizations are utilizing AI, a strategy that has gotten synonymous with AI lately. AI is a mechanized method utilized by a PC to encourage itself to settle on choices utilizing preparing information. Preparing information is the fuel of AI, as depicted by Andrew Ng of Stanford University.

Babylon Health and IBM Watson have both structured frameworks that produce this “fuel” from their clients. As they draw in more clients, they will create better bits of knowledge. This system impact is a temperate circle where the item turns out to be better as it includes more clients. The drawback of items with organize impacts is that they are famously hard to kick-start. Simply think that it is so difficult to get the initial barely any individuals for a dating site.

Babylon Health and IBM Watson have each collaborated with set up players to defeat this test and get the fuel they must prepare. Babylon Health is bootstrapping their item with assistance from a UK NHS organization. The UK NHS is looking for approaches to relieve their primary care physician deficiency and will preliminary Babylon’s chatbot for a half year in North Central London, a territory covering 1.2 million residents. IBM Watson is cooperating with Memorial Sloan Kettering to help train Watson on the abundance of clinical data and therapeutic aptitude that the middle is known for.

Regulatory risk: A potential challenge:

With AI-fuelled human services items indicating so a lot of guarantee, one may anticipate that guideline should go rapidly through the FDA. In any case, the FDA is right now battling. As the Wall Street Journal puts it:

“How on earth would you say you will manage programming that learns?”

Current guidelines need principles to evaluate the wellbeing and adequacy of AI frameworks, which the FDA has endeavoured to address by giving direction to surveying AI frameworks. The principal direction orders AI frameworks as “general wellbeing items”, which are inexactly managed as they present okay to clients. The subsequent direction legitimizes the utilization of certifiable proof to evaluate the presentation of AI frameworks. In conclusion, the direction explains the principles for the versatile structure in clinical preliminaries, which would be generally utilized in surveying the working qualities of AI frameworks.

Notwithstanding these difficulties, things are looking bullish for AI-fuelled medicinal services. Babylon Health and IBM are just two of numerous new activities that are expanding the range of medicinal services by strengthening the parts that don’t scale: specialists. While every one of these organizations has their very own perspective on the future, they all concur that AI will let our constrained restorative experts carry the best medicines to the best number of individuals. Particularly when the best treatment is acting before we become ill.


Artificial intelligence relies upon advanced information, so irregularities in the accessibility and nature of information confine the capability of AI. Likewise, huge registering power is required for the examination of huge and complex informational indexes. While many are energetic about the potential employments of AI in the NHS, others point to the down to earth difficulties, for example, the way that medicinal records are not reliably digitized over the NHS, and the absence of interoperability and institutionalization in NHS IT frameworks, computerized record keeping, and information labelling. There are inquiries concerning the degree to which patients and specialists are OK with advanced sharing of individual wellbeing data. Humans have properties that AI frameworks probably won’t have the option to genuinely have, for example, compassion. Clinical practice regularly includes complex decisions and capacities that AI as of now can’t imitate, for example, logical information and the capacity to peruse social cues. There is additionally banter about whether some human information is implicit and can’t be taught. Claims that AI will have the option to show self-governance have been addressed on grounds this is a property basic to being human and cannot be held by a machine.

Overall, artificial intelligence advances are being utilized or trialed for a scope of purposes in the field of social insurance and research, including identification of illness, the executives of constant conditions, conveyance of wellbeing administrations, and medication disclosure. Simulated intelligence advances can possibly help address significant wellbeing challenges yet may be restricted by the nature of accessible wellbeing information, and by the powerlessness of AI to have some human qualities, for example, empathy. The utilization of AI raises a few moral and social issues, a significant number of which cover with issues raised utilizing information and human services advances more extensively. A key test for future administration of AI advancements will guarantee that AI is created and utilized in a manner that is straightforward and good with general society intrigue, while animating and driving development in the part.

This article is co-authored by Prof Raul Villamarin Rodriguez, Aakriti Jain, Mohit Mohan Saxena, Epari Shravan and Vaibhav Yadav, Universal Business School.

Machine Learning

Supply Chain 4.0: AI and Robotization

Automatic processes, machine learning, and robotization force a constant updating of the knowledge for those professionals responsible for logistics in MNCs and SMEs.

On one hand, ERP systems have become the nerve center of companies and within these the logistics sector is the true heart and engine of all activity. Specialized logistics services companies proliferate and create models that are responsible for the dynamization of international trade, both B2B and B2C.

These systems transcend the mere management we have known so far, processing thousands of data generated from all departments of the company or collected by automata. This data is systematically analyzed and managed by algorithms that generate automatic decisions, learning from successes and errors.

But if there is something that is causing the entire logistics process to change in a radical way, it is robotization. Until date, a large number of personnel dedicated to logistics processes such as product and merchandise handling, order issuance, warehouse and inventory control or replenishment management were needed. However, robots are replacing these functions and ending, to a large extent, with the need for labour. These are capable of carrying a load of up to 500 Kg from one end to another of the warehouse. And even move it from one warehouse to another. In the same way, they can rotate 360 ​​degrees on its axis, rise to load merchandise or deposit it gently at any point. We need to consider that they do much faster than humans without getting sick or requiring rest.

Within five minutes, the robots recharge and have a range of 4 or 5 hours. So they can cover a full 8-hour shift with a total of ten minutes of recharging. Consequently, they can perfectly cover three daily shifts. It has no conflict between them or collective claims and they are immediately replaceable in case of breakdown or need for maintenance.

The logistics robotization process is generating profound changes in the business model that affect all areas, especially the human resource management.

According to reports from the World Economic Forum, by 2025 the replacement of human personnel with robots in all basic professional areas will have reached 52%. This means the loss of countless unskilled jobs. That will be compensated with the creation of 58 million qualified jobs, necessary for the robotic revolution, in the next 10 years.

It is not difficult to think that technical qualifications will be one of the challenges to overcome in this whole process.

Featured examples of robotization in large multinationals

Two of the most prominent precursors within this logistics reorganization have been two giants of online commerce; Alibaba and Amazon.

Amazon’s experience

Amazon has more than one hundred thousand robots dedicated to managing the orders of its customers and all its stores are currently automated. Quite the opposite of destroying employment, the company has doubled the workforce since 2016, currently having more than 500,000 workers.

Kiva robots, used by the firm, can easily replace physical work and repetitive activities that are easily programmable. But the same does not happen with another set of required skills that are demanded in new positions to add value.

Alibaba’s model

With its logistics model, it has facilitated the penetration of thousands of companies in different international markets. Process automation and artificial intelligence are the engines of productivity in our day and this, in turn, is the key factor in competitiveness.

Only through these logistic processes is possible to manage the huge volume of orders for days like Black Friday or the Alibaba shopping festival.

JD’s case

Another Chinese marketing giant, JD has recently surpassed Alibaba with a warehouse capable of processing more than 200,000 orders daily with only the supervision of 4 people. The objective of this company is to provide service to all of China on the same day as long as the order is generated before 11 am.

The company has not only invested millions in robots for warehouses but also done so in the incorporation of automatic systems in trucks, means of transport and distribution drones.

Prof. Raul V. Rodriguez

Machine Learning

The Future of the Maritime Logistics Industry: Unmanned ships from 2020

There are no drones only across the sky but also on land and sea and Rolls Royce has focused on the latter for its commercial strategy as far as vessels are concerned.

The company, which no longer manufactures cars -transferred the automobile division to BMW- is a conglomerate that operates in the aeronautical, aerospace, maritime and energy sectors. They have a clear-cut commitment to the seas: launch unmanned ships by mid-2020.

In the meantime, the Rolls-Royce Blue Ocean research team has already launched a virtual reality prototype in its office in Alesund, Norway, which simulates the views from a ship’s command bridge in 360 degrees. The manufacturer hopes that ship captains can maneuver hundreds of unmanned ships from the ground, without any need to approach the sea.

The idea is that during this year the first fleet of unmanned ships will be built. The first would be tugboats or ferries, boats that make simple, short-sized journeys in controlled environments. At first, all risks must be minimized, in order to avoid any possibility of unforeseen events.

The next stage would be the launch of cargo ships, with increasing complexity, especially because they sail in international waters. As of today, there is no legislation that covers unmanned commercial shipping. And the approval of international regulation is always slower than that processed by individual countries.

Unmanned ships, according to Rolls Royce, will reduce operating costs by 20%. Companies, therefore, buy ships to increase their profit margins. The other side of technology is the possible loss of jobs. It will not be necessary to have a crew either a large contingent of security personnel. However, piracy will surely remain a threat that requires the presence of minimal security personnel while keeping in mind that there will not be as many lives at stake in the absence of crew members as the risk for cargo theft.

Although, Rolls-Royce pointed out that new jobs will be created. The operations will have to be performed from the ground. It is an unmanned craft, not autonomous. Cybersecurity will be a key element assuring secured communications links between the ship and land, hence new profiles will be necessary.

By replacing the control bridge along with the other systems where the crew is usually accommodated – including electricity, air conditioning, water, and waste treatment system- the ships will withstand more cargo, reducing costs and increasing revenue. In addition to this, according to the initial calculations, these ships will be 5% lighter and consume between 12 to 15% less fuel ensuring a greener performance. Similarly, electric fuel-free ships are being researched in order to consider their implementation.

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Machine Learning

Will Artificial Intelligence reach the level of the human intellect by 2040?

Technological singularity is a hypothesis that predicts that there will come a time when artificial intelligence will be able to improve itself recursively. In theory, machines that are capable of creating other machines even more intelligent, resulting in intelligence far superior to human beings and, which could be even more shocking, beyond our control.

AI, Machine Learning, Neural Networks… these are terms that transmit feelings which are equally of hope and fear of the unknown.

In the next 20 years, there will be more technological changes than in the last 2 millennia. The technology is much faster than the brain – a calculator multiplies 5-digit numbers in tenths of a second – but it works differently, for example, it does not have the level of connections equivalent to that of neurons in a human brain.

However, if the exponential speed of Moore’s law does not stop and the investigations of neural networks of giant corporations such as Google continue to advance by 2040 the degree of technological integration in our lives will far exceed the capacity of the human brain.

The word singularity was taken from astrophysics: a point in space-time – for example, inside a black hole – in which the rules of ordinary physics are not lost. It was associated with the explosion of artificial intelligence during the 1980s by science-fiction novelist Vernor Vinge. At a NASA symposium in 1993, Vinge predicted that in 30 years there would be technological means to create superhuman intelligence called Singleton which refers to a “world order in which there is a single decision-making entity at the highest level, capable of exerting effective control over its domain and preventing internal or external threats to its supremacy”. In addition to this, he assured that, shortly after, we would reach the end of the human era.

Throughout history, some technological advances have caused fear. The fear of the new and the unknown is understandable, however, all technologies can be modified for good or for evil, as you can use fire to heat and cook food, or to burn people

In the case of the singularity, it seems clear that one must be cautious, regulating its development but without limiting it and, above all, trying to ensure that this future artificial intelligence learns from ethical and moral values, as well as from mistakes and successes of the species. We must be clear in our conception of the term. Human beings and machines are meant to co-exist in symbiosis and not rivalry. 

Mortality as an “option” by 2045?

On the other hand, we could analyze if mortality will be “optional” by 2045. Google has already started extravagant research initiatives as they realized that curing aging is possible and that is why they are creating companies such as ‘Calico’ or ‘Human Longevity’, which are investigating it, but also non-profit organizations such as the Methuselah Foundation. It is evident that the possibilities are real since immortality already exists in nature. Some cells are immortal and the stem cells affected the quality of reproducing indefinitely, just like cancer cells.

One of the steps to achieve this is to fully comprehend the structure of incurable diseases today, and then eradicate them. Thus, as it happens with HIV, a controllable chronic disease, or diabetes. We must propose the same with aging: turn it into a controllable chronic disease, and later on, cure it for good. It is essential to begin human trials with rejuvenation technologies that have been shown useful in other animals leading to advancements in human clinical trials as well. 

Prof. Raul V. Rodriguez is an Asst. Professor at Universal Business School.