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RPA and GDPR: Security Governance in the Automation Era

RPA and GDPR: Security Governance in the Automation Era

The data on security breaches is overwhelming on many fronts. Over a billion records of consumers have been compromised since 2005. The total number of breaches in the period is threatening at around 8000. As late as 2017, big companies like Target, Equifax, and Neiman Marcus could not shield themselves from data breach attempts. Mind you, one of these is a top national credit reporting agency.

Noted analyst Avivah Litan predicts the following instances of misuse for the stolen data:

  • The data can get tossed around in an endless sell-and-resell loop of underground data piracy
  • Sensitive data can be used to steal bank accounts from customers
  • Identity thieves can use the data to update their existing records of targeted individuals
  • Adversarial nation states can use the data to disrupt peace or launder money out of the US

None of these constitutes stray casualty. The cumulative implications of the breaches are beyond grave. In fact, it is very difficult to quantify the damage dealt by these breaches to the society at large. That is where the General Data Protection Regulation (GDPR) swings into action. It gives consumers greater control over their own data while making corporates bite the bullet on their data processing practices.

What is the GDPR?

The GDPR is a regulation adopted by the European Union. It lays out the norms for data protection and privacy for the individuals that live in the European Union. It is one among the series of regulations that have helped formalize governance around security concerns of the average consumer.

In addition to strengthening consumer rights, GDPR aims at formalizing security standards that companies must establish to protect the data of their consumers.

Every organization functioning out of Europe and non-European organizations that collect the data of European citizens are expected to comply with the GDPR. The latest GDPR guidelines regulate how personal data is used, processed, stored, and deleted.

The GDPR also lays out that data subjects can request for both access and real-time usage information from organizations. If there’s any breach involving the personal data of users, it must be reported to the appropriate authority that oversees the regulation.

Security Governance: The Onus is on the Enterprise

At the crux of the GDPR is the impetus the regulation puts on enterprises to do all things necessary to protect consumer information. This has forced every enterprise software vendor to re-evaluate their policies regarding storage and management of sensitive user data.

This is where Robotic Process Automation (RPA) is impacting the industry in a big way. RPA platforms like Automation Anywhere are instrumental in offering comprehensive features in security and reliability. Starting with automation at once promises the following benefits for organizations:

  • Data encryption at all levels – when the data is in memory, in motion, or at rest.
  • A robust security framework (either built-in or third party) that guarantees security in the management and storage of user information. As a default practice, machine that store user credentials meant for critical purposes and the machines that run the software should always be exclusive.
  • Analysis of codes on both static and dynamic parameters, including manual pen testing for unbreakable application security.
  • Seamless enterprise based authentication system integration
  • Expansive logs of audits to support forensic analyses and audit processes
  • Secure operations that that make sure data is not exposed to business process threats during standard execution of processes

RPA platforms work with many ERP tools and in effect touch extensive sets of data within your organization. In case you are already using an RPA platform, make sure to check with them on GDPR compliance and the security measures they follow to ensure compliance.

How is RPA Easing up GDPR Implementation?

The first and absolutely unavoidable threat with manual processing of customer data is the guarantee of human errors. It does not really matter what level of security you follow. Even the slightest margin of error means that the organization is at the risk of non-compliance.

With RPA, you can automate the process defined by the legal and business teams to become GDPR compliant. Here is a collection of ways in which bots are helping enterprises with GDPR compliance:

Audit Logs

Enterprise RPA platforms are loaded with audit logs which monitor every operational process, creating logs for users and events at every stage of a given process. When there’s a data breach, audit logs swing into action with recurring spells of root cause analysis. What follows is routine forensic analysis to recognize and thereafter report the breach.

Content that relates to specific internal or external events can be gathered concurrently in real time. This comes in especially handy in case an organization is attempting to decode a fraudulent activity.

Documentation of data

There’s a lot of data pouring in from devices, sensors, and systems at the office. From the organizations perspective, it must be able to document all the data that is held in its directory, along with the source of its origin. The organization must be able to submit updated reports to the authorities in charge of data protection. GDPR mandates companies to purge personal data once it has crossed the holding period.

This is another area where RPA can help organizations by using bots that automate the process of masking PII data that is identifiable across applications. For the PII data that does not adhere to established policy, Natural Language Processing (NLP) lets bots recognize such data and generate alerts that help in intercepting the issue.

Data Breaches

GDPR makes it mandatory that subjects affected by data breaches be informed about it within 72 hours. For data breaches of a magnanimous nature, sending out information to everyone involved within 72 hours can become almost impossible. Imagine the case of Equifax, where 143 million users directly affected by the breach.

On the flip side, it is way easier to automate software bots to perform the job. In most instances, it does not even take 72 hours and makes sure the security governance timeframe is met.

Right to Access Information

European customers can request to access their information and know how an organization stores and uses the information. GDPR guarantees this right to all European consumers. If an organization wants to do this manually, it would need a dedicated team of individuals. Plus, every individual on the team must have access to such information.

It is way easier for bots to navigate through different systems and pull out data relevant to every user in question.

Right to Information Deletion

If a user requests an organization to dispossess their personal information, GDPR mandates the organization to delete such information promptly. Consider there is no automated process to do this. An employee or a team will have to access the information and then delete it from dozens of applications. Bots can not only pull out the relevant information on users but also email the report back to the concerned customers.

Some Data Cannot be Seen

There are legacy systems hiding data more than a decade old. Data can be accessed from these systems when needed. However, it’s never been as important to uncover sketchy data as it is now. RPA is the most convenient way to integrate the current technology platforms with legacy systems. Automation is also perhaps the only way to document and recognize available data that might be the cause of non-compliance.

Most companies are still taking their own sweet time understanding and dissecting the General Data Policy Regulation. At this, there is the threat of flooding of requests by consumers. Adhering to these requests will be compulsory. Doing it manually will mount up heavy costs on the administration. But the fact is responding to such requests might only be subject to a few well-defined requests. That makes it a great process for RPA to flex muscles.

The crux of it is organizations will have a hard time maintaining GDPR compliance in the absence of RPA. RPA solves security governance through GDPR wholly with the promise of zero errors.

10 Real World Examples of Deep Learning Models & AI

For the vast majority of us, concepts like deep learning and Artificial Intelligence are still alien. Most people who come across these terms for the first time react with mixed feelings of skepticism and intimidation. How can we make machines learn and execute jobs meant for humans?  What really explains an entire industry bent upon making machines behave like humans?

While these questions are important and call for discussion, we can easily do away with much of the skepticism. That is, if we are willing to look at some real world applications of deep learning and artificial intelligence. In this article, we show you ten ways in which artificial intelligence and deep learning are turning wheels across industries.   

Where does deep learning come from?

Machine learning and deep learning are both subsets of artificial intelligence. Deep learning is the evolved and advanced phase of machine learning. In machine learning, human programmers create algorithms that learn from the data and derive analyses.

Deep learning is different from machine learning in that it works on an artificial neural network which closely represents a human brain. The same network allows machines to analyze data just the way humans do. Such machines with deep learning capacities do not require to act upon the instructions of human programmers.   

Deep learning is made possible through the ginormous amounts of data that we create and consume daily. Every deep learning model makes extensive use of data to facilitate data processing.

10 Real World Applications of Deep Leaning

Here are ten ways deep learning is already being used in diverse industries.

1. Computer vision

High-end gamers interact with deep learning modules on a very frequent basis. Deep neural networks power bleeding-edge object detection, image classification, image restoration, and image segmentation. So much so, they even power the recognition of hand-written digits on a computer system. To wit, deep learning is riding on an extraordinary neural network to empower machines to replicate the mechanism of the human visual agency.

2. Sentiment based news aggregation

Carolyn Gregorie writes in her Huffington Post piece: “the world isn’t falling apart, but it can sure feel like it.” And we couldn’t agree more. I am not naming names here, but you cannot scroll down any of your social media feed without stumbling across a couple of global disasters – with the exception of Instagram perhaps.

News aggregators are now using deep learning modules to filter out negative news and show you only the positive stuff happening around. This is especially helpful given how blatantly sensationalist a section of our media has been of late.

3. Bots based on deep learning

Take a moment to digest this – Nvidia researchers have developed an AI system that helps robots learn from human demonstrative actions. Housekeeping robots that perform actions based on artificial intelligence inputs from several sources are rather common. Like human brains process actions based on past experiences and sensory inputs, deep-learning infrastructures help robots execute tasks depending on varying AI opinions.

4. Automated translations

Automated translations did exist before the addition of deep learning. But deep learning is helping machines make enhanced translations with the guaranteed accuracy that was missing in the past. Plus, deep learning also helps in translation derived from images – something totally new that could not have been possible using traditional text-based interpretation.

5. Customer experience

Many businesses already make use of machine learning to work on customer experience. Viable examples include online self-service platforms. Plus, many organizations now depend on deep learning to create reliable workflows. Most of us are already familiar with the use of chatbots by organizations. As this application of deep leering matures, we can expect to see further enhancements in this field.

6. Autonomous vehicles

The next time you are lucky enough to witness an autonomous vehicle driving down, understand that there are several AI models working simultaneously. While some models pin-point pedestrians, others are adept at identifying street signs. A single car can be informed by millions of AI models while driving down the road. Many have considered AI-powered car drives safer than human riding.

7. Coloring illustrations

At one point, adding colors to black and white videos used to be one of the most time-consuming jobs in media production. But thanks to deep learning models and artificial intelligence, adding color to b/w photos and videos is now easier than ever. As you read, hundreds of black and white illustrations are being recreated in colored form.

8. Image analysis and caption generation

One of the greatest feats of deep learning is the ability to identify images and generate intelligent captions for them. In fact, image caption generation powered by AI is so accurate that many online publications are already making use of such techniques to save time and cost.

9. Text generation

Machines now have the power to generate new text from the scratch. They can learn the punctuation, grammar, and style of a piece of text and pen down effective news pieces. Robo-journalists riding on deep learning models have been producing accurate match reports for at least three years now. And the skill isn’t limited to match report writing exclusively.

AI-based text generation is fully equipped to handle the complexity of opinion pieces on issues concerning you and myself. As of now, text generation has helped create entries on just about everything from children’s rhymes to scholarly topics.

10. Language identification

At this point, we are looking at a preliminary stage where deep learning machines can differentiate between different dialects. For example, a machine will make the decision that someone is speaking in English. It will then make a distinction based on the dialect. Once the dialect has been established, further processing will be handled by another AI that specializes in the particular language. Not to mention, there is no human intervention in any of these steps.

These were just a few applications of deep learning that exist already. The further growth of deep learning models will bring to us many more uses of artificial intelligence around us. At Futran Solutions, we work with top-of-the-line AI resources that make the above industry applications of AI come to life. Contact us today to find out more about our RPA, AI, and deep learning solutions.

Jyoti Vazirani is the co-founder and CEO of Futran Solutions. She is a certified SAFe Agile coach and an out and out deep learning enthusiast.

Industry 4.0: The Era of Cyber-Physical Systems and Intelligent Analytics

Self-driving cars. Intelligent bots. Neuro-tech psycho-development. Genetic editing. Geoengineering. Artificial lifeforms. Mobile supercomputing of a surreal standard. Welcome to Industry 4.0.

Industry 4.0 or the Fourth Industrial Revolution is not a purely industry-specific phenomenon. Like the three industrial revolutions of the past (water and steam-powered motors, electricity-powered assembly lines, and computerized systems), the fourth industrial revolution will challenge the very way we work, live, and connect with one another – and with bots. The adoption and amalgamation of distinct values like the internet of systems, internet of things, and the cyber-physical grid will redefine and reform the very fabric of industry as we know it.

First off, we need to understand that Industry 4.0 or the Fourth Industrial Revolution is not a gimmicky marketing stunt pulled off to sell a new technology. It’s a living phenomenon described in very fine detail by Professor Klaus Schwab, founder and executive chairman of the World Economic Forum in his book, The Fourth Industrial Revolution. In his book, Professor Schwab argues:

The changes are so profound that, from the perspective of human history, there has never been a time of greater promise or potential peril.

While Schwab highlights that the dawn of the fourth industrial revolution promises exponential growth, he also points out a set of risks that constitute the “potential peril” in the above statement. We’ll get to that in a minute.

What constitutes Industry 4.0?

By now, it is understood that both computing and automation have leaped to the next level with Industry 4.0. Core principles of robotics are connected to computer systems, which are in turn equipped with ML (machine learning) algorithms. The resultant loop can learn, control, and execute robotics and allied operations with minimum (almost negligible) human supervision.

Then, there’s the introduction of what is called the “smart factory.” Within the setup of a smart factory, cyber-physical systems have the capability to make decentralized decisions while monitoring physical processes. The wireless web connects the physical systems enabling interaction with one another and with humans.

By this structure, a setup can be considered within the Industry 4.0 structure when it has the following four attributes:

  • Independent (or decentralized) decision making: removing the “approval” cap off simple, obvious decisions for cyber-physical systems, allowing them to be as autonomous as possible.
  • Sound technical assistance: assisting humans with technical aid for tasks that are either too labor-intensive or too dangerous for humans along with the general ability to assist humans with decision making and problem-solving.
  • Transparency of information: contextualizing information and datasets through systems that create virtual copies of the physical world through sensor data.
  • Heterogeneous network interactions: devices, sensors, machines, and humans connecting and communicating with one another in a complex yet seemingly manageable environment

The concerns with the major shift in Industry 4.0

If your organizational infrastructure has woven through the above elements, you are already riding the Industry 4.0 wave. But like it has happened every time a new technology has set in, a set of concerns follow the benefits.

First off, the natural extension of allowing new systems entry into existing data banks brings the threat of security breaches. The more access you give to these systems, the more data security issues you will have to deal with. Plus, proprietary production knowledge presents an adjacent concern related to IT security.

The degree of stability and system reliance required for successful cyber-physical bonding will be very high. Such states can be difficult to obtain as well as maintain. Additionally, we have the challenge of maintaining the integrity of manufacturing and production processes with dwindling human control.

Perhaps the most crucial challenge in all of this will be the loss of high-paying human jobs. We simply cannot brush under the carpet the corporate accountability for humans in a world of bots. Finally, there’s always the risk of running into expensive production outages arising out of technical inconsistencies.

First-time blues matter less than the expansive potential

The singular most challenging part of implementing new technology across industries is the lack of experience. The systemic shortage of manpower in implementing these new changes would be augmented with the general reluctance of stakeholders in trusting new technologies upfront. Low trust naturally translates into lower investments.

Irrespective of the initial round of suspicion, there’s a lot of benefits offered by the 4.0 model. More importantly, the benefits outlive the concerns by a long shot. Safety of human workers in dangerous work environments can be dramatically improved. We will exercise far greater control over the supply chains when there is processed data at every level of the delivery process.

One thing is certain. The cyber-physical combination kind of guarantees increased productivity and faster deliveries. With that, the revenue, market share, and profits – all shoot up.

Industry 4.0 demonstration city

Mayor John Carnely of Cincinnati, Ohio has declared that Cincinnati, Ohio will be an Industry 4.0 demonstration city. The primary objective of the proclamation is to create a hub for investment and manufacturing within the Industry 4.0 environment. The resolution also thanked the contributions of Prof. Jay Lee and the Center for Intelligence Maintenance Systems for their positive contributions to the global revolutions surrounding Industry 4.0. The Cincinnati move is not just strategic but also historical given the potential leverage it provides to the manufacturing hub in the city.

Besides the Cincinnati move, reports suggest that emerging economies like India will reap great benefits of Industry 4.0 practices, largely because of readily available resources and the willingness to participate in new technologies.

Are we ready for the transformation?

To be honest, most industries will follow conventional practices unless there’s a standout example of blazing success in the Industry 4.0 model. But I am afraid much time will be lost with respect to realizing the largest gains promised by the revolution. Companies that show the courage to dive in fast will well and truly emerge as the frontrunners in maximizing profits.

There are additional issues that play beyond the willingness to adapt to change. Legacy systems cannot be overhauled in a night. The efficacy of tenured systems with proprietary applications should also continue to be just as important as they are today.

The analysis of big data and dark data will be one of the first challenges that the 4.0 architecture will need to address. We presently have immense volumes of data generated by digital systems, existing sensors, and other existing equipment. Much of these data banks that are presently unaccounted for will influence decision making in the industry.

Robotic Process Automation and Industry 4.0

That we are generating more volumes of global data than ever is one side of the story. ERP systems, bots, sensors, and even cookies are collecting pools of data that is currently unused. Most manufacturers are concerned about making this connection between machines from the production unit and back office systems. Finding a way to channel the unstructured data requires more than conventional computing. It requires software-based intelligent automation.

The best bet about RPA is that it seamlessly gels in with the existing IT setup inclusive of hardware and legacy applications. Large volumes of enterprise data can be indexed and structured using RPA. When clubbed with AI, RPA presents learning capabilities with advanced data models to organize and classify all the information, thereby making it valuable. On the whole, the platform visualizes these insights and eventually helps in predicting the future.

Notwithstanding the magnanimity of the promise presented by the Industry 4.0 model, the realization of success can only come with the right RPA solutions and resources. Futran Solutions specializes in delivering composite RPA solutions and resources. To expand upon a wholesome list of RPA offerings, Futran recently partnered with Automation Anywhere – a leading platform for implementation of automation solutions across the industry.

Jyoti Vazirani is the co-founder and CEO of Futran Solutions. She is a certified SAFe Agile coach and an out and out machine learning enthusiast.

What Does Corporate Accountability Stand for When Bots Do All the Work?

What Does Corporate Accountability Stand for When Bots Do All the Work?

Only one event commands the same absolute degree of certainty as death – disruption of the status quo. Unless you concede that transformation is the new status quo. In every industry.

We now live in an era where the promise of unprecedented growth is almost unchallenged. It takes no second guessing to formulate that such growth cannot be realized without an increasingly digitized workforce headed by business leaders that are not afraid to weave AI, RPA, and cognitive machine learning technologies into existing processes.

But like Jyoti Vazirani had stated in a previous article, the more we lean toward automation and machine learning and integrate them to our core business processes, the faster we realize that there’s an expansive human workforce calling for imminent, preferably immediate consideration. What happens to the corporate accountability of our human resources? More importantly, what happens to our human resources?

For the start, let’s not bluff ourselves with arguments that make rats and bats crack up. Intelligent, emotionless bots are far more ruthless when it comes to beating known standards of efficiency and productivity. It’s time the high and mighty leaders in business work to create the right synergy between humans and bots.

The audacity of bots

What has for long been an expectation and a speculation is now looking at us in the face as a guarantee of sorts. Bots will outperform humans, they’ll champion cognitive functions, and in the very near future even develop the capacity for thought, albeit with a debatable degree of independence. That explains why even as business leaders are running hay and hill behind robotic process automation, the workforce at large is unwelcoming of the change.

The madness of reaping early-bird profits from automated processes is so extremely insane that almost no business leader is ready to acknowledge the unavoidable employment crisis, let alone starting a meaningful dialog on it.

Crisis #1: Saving the value-based ecosystem

In the last two decades, organizations have successfully embedded a sense of value in their core missions and brand philosophies. Fortunately, the largest part of this change was brought about by the technology companies. If your mind is already reading out names of tech giants, you see my point.

Ironically, the first and perhaps the biggest industry that will be faced with the workforce imbalance ushered by bots is the technology industry – because of obvious reasons. How, when, and if at all the industry stands up to the challenge is shrouded under expansive dubiety.

If the technology industry – the very one that created the bots in the first place – does not act swiftly to save the value-based corporate ecosystem, it will become doubly difficult for other industries to follow suit.

Crisis #2: Maximizing convenience and minimizing pain

That’s just what bots will do. For their corporate masters. Will the effect be the same for lower-rung employees? Hardly. Here are some more questions that must be answered:

  • Will we create new jobs for displaced employees – jobs that we didn’t know existed?
  • Are we sure that bots guarantee a better and more sustainable corporate future?
  • Is there a long-term reskilling program that gives employees the flexibility to try out newer careers?
  • Does the combined human-bot workforce turn out to be as effective as it looks on paper?
  • Do business leaders even care?

There’s no definitive answer to any of these questions. But we must remind ourselves that history will remember us not for creating bots, but for what we did to humans after bots were created.

Crisis #3: Weighing down by Peter Principle

The Peter Principle is a benchmark corporate ideal laid out by Canadian educator Dr. Laurence J. Peter. It states that in organizational hierarchies, employees rise up the ranks through promotion until they are promoted to a position for which they are incompetent. In effect, it highlights the logical assumption that save a few exceptions, one individual cannot have mastery over many diverse fields within a corporation.

The principle also states that every position in the hierarchy of an organization will at some point be filled by people who are incompetent to fulfill their job roles in those respective positions. Dr. Peter also stressed upon the fact that such outcomes might not be related to the general incompetence of employees. It’s largely because new positions might require additional skill-sets which cannot always be imparted through training.

The Peter Principle has been as true lately as it was in 1968 when the term was coined. But there were no automated bots in 1968. So the principle applies exclusively to human competence, and to wit, to human employees.

Here’s how the principle unfolds with the bot-human amalgamated workforce:

  1. It will still apply to humans only because bots are not likely to receive promotions
  2. A large part of the human workforce will be saved from the Principle because they will no longer exist as a part of the workforce
  3. Of the humans that will still be a part of the workforce, very few will be engaged in the lower rungs of the hierarchy; the long chain of systematic promotion to the top will break and the Peter Principle will lose much of its premise
  4. Since there will be very few human employees, most of them will comprise of the top management of the organization. We can safely assume they will come with enough training required for their positions

If you’re reading this right, the Peter Principle might cease to even exist if the human-bot work grid is laid out at large. A solid, 50-year old tried and tested corporate theory based solely on logical deduction, human psychology, and organizational observation might die a sudden death without people even noticing. If this doesn’t send the alarm bells ringing in wild abandon, no corporate activist screaming off rooftops ever will.

Back to the drawing board

Let’s resume thought with the truth.

The sun is setting on thousands of employees. Nothing in the corporate universe is as lucrative as the prospect of making big money in small time. Most corporations impart skill-training to employees not really to help them grow their skill set. But to extract greater competency from the same resource. If they can replace skill training with an additional program on an automated bot and save both time and money, make no mistake – they will.

You could argue that the Top 100 companies might actually retrain staff for new jobs. I say yes, they very well may. But the best of the organizations have laid off staff by the hundreds for much smaller reasons. Plus, retraining might not automatically amount to retaining. More like “we teach you fishing and there’s a lake at 6 o clock.”

Like Jyoti had written earlier, the human workforce cannot be saved without government intervention. Might sound grim, but it’s just as necessary. Tech and other industry giants will plant so many trees that no one will ever talk about the human workforce they uprooted. They’ll make so many of those feel-good inspirational videos that no reporter will cover the plight of the jobless that were once employed with them. But are the governments even listening? They won’t unless people who care about jobs speak up. For very soon, jobs will make way for tasks in the most cruelly literal way.

Futran Solutions supports robotic process automation. We are a pro-technology company. And we believe that if a technology deserves to go viral, we must do our part in making it viral. We provide a range of RPA and AI solutions to industries across the board. Adjacently, we run a series of training programs to aid the displaced workforce.

Drop us a line to know how we can help you with RPA consulting and project implementation.  
Krishna Vemuri is the co-founder of Futran Solutions and the CEO of the up and coming tech startup Onata. He writes on technology industry dynamics and the rather eclectic tantrums of his husky, Loki.
Five Applications of Machine Learning in the Pharma Industry

Five Applications of Machine Learning in the Pharma Industry

Machine learning seamlessly is integrating with other industries right before our eyes. Like with so many so many other industries, more data means greater effectiveness in the pharma industry. A McKinsey report estimates that machine learning and big data could generate a combined business value of $100B annually. The value optimization involves optimal innovation, better decision making, greater efficiency for clinical/research trials, and additional tool creation for medical professionals.

What is the source of so much data? Regular streams like research and development, clinics, physicians, patients, and caregivers do their bit. The disparate origin points of data sets form a large part of the problem when we talk of synchronizing all the data and improving the healthcare industry as a whole. The core of the problem is to find ways to effectively collect different types of data sets for better treatment, analysis, and ultimately treatment.  

Today, the applications of Machine Learning are sprouting in manifold ways. All these applications give us a glimpse of a future where the analysis and synchronization of data is already a reality. Here’s a collection of some of the most important applications of machine learning in the pharma industry.

1. Behavioral Modification

With machine learning, personalized medication will soon be a reality. Such treatment is based on individual health data along with a decent dose of predictive analysis. In fact, this is one of the most hotly worked on topics on machine learning and behavioral modification.

At the moment, this area is dominated largely by a combination of genetic information and supervised learning. This basically allows physicians to choose from a set of diagnoses. The next decade is crucial for health optimization with the help of machine learning. Micr biosensors will see a further rise in the application and there will be a similar rise in use remote monitoring capabilities.

A gamut of startups is emerging in fields like cancer identification and treatment. While the success of these innovations is still far from desirable, there is a good chance of a significant breakthrough in the coming decade.

2. Research and Clinical Trial

Machine learning has already tasted decent success in shaping direct research and clinical trial. The application of predictive analytics in identifying candidates for clinical trials could see a lot of additional data pouring in compared to the volume of data we see today. Clubbed with genetic information, this will help in quicker and more cost-effective trials in the coming days.

The application of Machine Learning also spans over access to real-time data for heightened safety. One critical area is the monitoring of biological signals for any visible sign of harm or fatality. McKinsey suggests that there is a whole lot of other applications that help in augmenting the efficiency of a clinical trial. This includes the discovery of the best sample sizes for greater efficiency.   

3. Drug Manufacturing

ML has tremendous scope in the early stages of drug discovery. The application starts at the initial screening of compounds and moves over to the predicted success rate based on a number of biological factors. We are also looking at discovery technologies in R&D like next-generation sequencing.

Precision medicine, a genre that involves mechanism identification for multifactorial diseases, is the frontrunner in this race. Since a lot of this research is based on unsupervised learning, the game revolves around identifying data patterns without the use of prediction of any kind.  

4. Identification and Diagnosis of Disease

Most of the present efforts in Machine Learning research for the pharma industry is geared toward disease identification. In a 2015 report released by the PRMA, the number of cancer medicine and vaccines in trial went over 800. The larger challenge, however, is to make justified use of all the data that comes out as a result of these studies.

It is here that the need for biologists working with information scientists and machine learning experts will become extremely vital. It doesn’t come as a surprise that the bigger players were the first to jump on the bandwagon. IBM Watson Genomics came into existence in 2016. It partnered with Quest Diagnostics to take rapid strides in precision medicine.   

5. Epidemic Control

At present, AI and ML technologies are also being used to monitor and foretell epidemics around the world. The predictions are based on satellite data, real-time updates on the social media as well as other sources. Scientists have already made use of artificial neural networks and support vector machines to predict outbreaks of malaria. The analysis took into account factors like average monthly rainfall, temperature, data points, and the number of positive cases.

The prediction of outbreaks (and their severity) becomes even more important in third-world countries where epidemics claim lives in the hundreds. This also alerts governments to implement prevention protocols and if needed quick treatment measures, too.

Futran Solutions works with a talented pool of resources that are ML experts in the pharma industry. Call us today to know how we can help you with project implementation and digital consulting in machine learning.

Jyoti Vazirani is the co-founder of Futran Solutions. She is a certified SAFe Agile coach and an out and out machine learning enthusiast.

AI, NI And The Future!

(Futran solutions is a company that offers RPA solutions. While respecting automation, Futran Solutions also participates in re-training of the employees who lose jobs owing to RPA)

Is AI thrusting upon us and our future generations a new technological, psychological and socio-economic change? Slowly but surely, are we humans becoming redundant for office, society and the universe at large? More terrifyingly, are machines really going to take control of humans? I believe we have looped this routine too many times on Hollywood to make it true? But we would all be too stupid to ignore the fact that the seeds have been planted already!

As companies prepare to make their businesses AI-powered right from smart virtual assistants to robotic process automation, we are looking at a tech-future with inevitably dwindling demand for human resource and Natural Intelligence.

At this point, we are a bit hard-pressed to figure how our LIMITED(!) natural intelligence will compete with the GOD-LIKE-POWER(?) of artificial intelligence. For once, let’s be honest and not pretend that there is no competition. There is tremendous competition already and it’s swelling up as we speak.

AI & NI: The Battle

When we speak about the competition between Artificial Intelligence (AI) and Natural Intelligence (NI), we are not expressly looking at a Terminator styled battle between human-made intelligent robots from the future and gym-vest wearing men and women of our times. In all reality, it will be the battle of employment between AI-powered robots (that are fast and accurate) and Natural Intelligence (NI) powered Humans of present times (that are slow and error-prone). A single AI-powered robot can potentially leave an entire team of 50, 500, or 5000(!) unemployed. The war has been waged; the winner is known! Only time confirms the facts!

Commercial manufacturing is already automated to a very large extent. Now imagine a situation where the smallest of factories are automated. The army is automated and no country has human armies. Governments are automated. The judiciary is automated where deep learning interprets cases and generates the judgments in seconds. And entertainment, too-imagine that you can virtually produce your own Marvel(like) movie for private viewing and that displaces thousands of people working for months together. Transport, heavy engineering, and several other industries-all automated to a point of no return.   

If such a future were to really transpire, Tesla would become a company with one employee. Elon, alone and his giant team of robots with greater caliber than human intelligence fill everything from boardrooms to shop-floors. Similar fates will embrace Amazon, and Google, and Microsoft. In that hypothetical situation, since all the workers are laid off and replaced with robots, humans shall stay back in their homes for the lack of work. They shall no longer be able to afford the luxuries, necessities, wants, and pleasures that they did when they were employed.

They’ll reach a stage where they have no money to BUY!

No jobs will soon mean no difference in affordability and no need to travel from Los Angeles to San Francisco in 30 minutes. No need to travel to Singapore in an hour. On the flip side, the sole shareholders of the big companies will lose business faster than we can imagine because no one will be able to buy their service/product. It’s pretty much the story of Bee Movie that was released in 2007. From that point, humans while battling machines, start living like our ancestors; growing their own food in their backyards, flocking to one another like never before to form the small habitats of self-reliant communities!

The situation has been discussed in detail about several eminent personalities of the Silicon Valley. While some people see no net change occurring, others have warned of grave consequences.  

How Does it Unfold?

Where do we stand speaking from a totally neutral plane of judgment? For the start, it is always wise to read from the pages of history when throwing light on the future. Remember the time when the coal mines were shut down and all the workers in the coal mines summarily lost their jobs! America became a global hero for taking the first big step in the fight against global warming at the cost of the joblessness of thousands of Americans.

The mine workers were never re-trained and re-employed. Not by the holier-than-thou corporates. Not by the holiest-among-the-holy governments of the world! So was the case when the factories were shut down!!

Aren’t mining and technology two totally different coins. Yes, on the face of it. But when you dig deeper, it is controlled by the same industry dynamics and the overall economics is same for every industry.

Companies will make a mad rush toward profits for shareholders, as the main job of the CEO is to increase the wealth of the investors. (A suggestion from us is; let the company’s performance also be measured by give-back-to-customer-index every year like the dividends to the shareholders, under the control of SEC!) To show investors an unimaginable P/E (price to earnings) ratio, corporations will lay off workforce by the hundreds and the thousands.  

The Takeaway 

For effective integration of Natural Intelligence and Artificial Intelligence, Governments may have to intervene with a legislation in favor of the “rehabilitation of the displaced workforce.” Without such planning mandated as a law, it will be easy for corporates to replace the people with AI-powered bots.

Futran Solutions specializes in RPA and is a proud partner of AA (Automation Anywhere). We provide solutions to our clients tailored to the RPA niche. In addition, as a part of our integrated solutions, we also offer re-training to the part of the workforce that will be affected by RPA. This allows employers to use the employees’ skill elsewhere and helps the latter explore other realms of technology without a day of unemployment.

Both the bridge and the boatman help us cross the river. The bridge promises to make the journey smoother and shorter. So we choose the bridge. But while the bridge is being built, the system must reward the years of service by the boatman and find him another career option before he wakes up jobless one day. How about training and rehabilitating him as a toll collector?

We, at Futran Solutions, support AI while respecting Natural Intelligence.

Jyoti Vazirani is the CEO of Futran Solutions. She can be reached at vjyoti@futransolutions.