How will Cybernetics And Artificial Intelligence build our future?

We live in a world where what was considered science fiction mere decades ago has become a reality. Global, wireless internet coverage, 3D printed technologies, the Internet of Things powered by AI-based assistants, and, of course, cyborgs, are all part of the reality we live in.

Cyborgs? Yes, those are real. Look at Dr Kevin Warwick. The man can operate lights, switches, and even computers with the power of his mind thanks to a handy chip implant. Neil Harbisson has overcome achromatopsia thanks to an implant that allows the artist to process colours in real-time on a level unachievable by anyone else on the planet. 

If you were to do some research, you’d find out that these pioneers are merely the tip of a cybernetically enhanced iceberg bringing up the real question: if we’ve already come so far, what awaits us in the future?

Cybernetics

Some of the most prominent projects diving into the exploration of cybernetics feel like they were taken from a cyberpunk novel. And yet they are real. More on the matter, they mark what is potentially the future of humankind as a species. 

Full-spectrum vision. Typically, humans believe that the way we “see” the world is the only possible way. Cybernetics engineers would beg to disagree. A simple injection of nanoantenna has proven to give lab mice the superpower of night vision. The experiment taking place in the University of Massachusetts has only recently moved towards practical studies of the effects the antenna have on rodents, but it has already proven itself to be among the first stepping stones towards cybernetically enhanced eyesight. Additional breakthroughs in the field have shown promising results in turning eyes into video cameras, or even development of artificial retinas capable of returning sight to the blind. 

Artificial brain cells. Modern advancements in the niche of cybernetics have already grown neurons – the basic components of a human brain – in laboratory conditions. These cells, artificially raised on an array of electrodes are proving themselves as a superior replacement to the hardware and software controllers we have today. 

More on the matter, scientists are already using brain-computer interfaces in medicine. Most are designed for therapeutic purposes such as the Deep Brain Stimulation designed to aid patients with Parkinson’s disease.

We will be able to use said technology to create connections that operate via the remaining neural signals allowing amputee patients to feel and move their limbs with the power of their mind. In some cases, as it was with Nigel Ackland, some might even go as far as to use the word enhancement when talking about top tier prosthetics.

Enhanced mobility. Stronger, faster, more durable – those are the goals of military-grade exoskeletons for soldiers that are already branching out into the medical niche and serve as prosthetics for amputee victims.  The combination of hardware and AI-based software eliminate the boundaries of human capabilities while minoring the vitals of the wearer in real-time. 

Technopsychics. The University of Minnesota is working on a computer to brain interface capable of remotely piloting drones. The machines can detect the electrical signals transmitted by the brain to control functioning machines in real-time. If you can navigate a quadcopter through an obstacle course using only the power of your mind today, imagine what we’ll be piloting remotely tomorrow. 

Nanorobots. Self-repair, growth, and immunity to diseases will soon be true thanks to a simple infusion of nanobots into your bloodstream. Modern researches explore the idea of developing your blood cell’s little helpers that can be controlled in the cloud from your smartphone!

Artificial Intelligence

As you may have deduced from the examples above, the advancements in the cybernetics niche are directly related to the progress we make with Artificial Intelligence or Machine Learning technologies. 

We need the software capable of driving the hardware to its limits if we are to dive deeper into cyborg technology. Artificial Intelligence is supposed to become the bridge between the man and the machine according to prominent research such as Shimon Whiteson and Yaky Matsuka. These scientists are exploring new ways AI can help amputee patients to operate their robotic prosthetics. 

Furthermore, AI is expected to take control of machines doing sensitive work in hazardous areas. According to BBC, we already have smart bots capable of defusing bombs and mines yet they still require a human controlling them. In the future, these drones (and many more, responsible for such challenging tasks as toxic waste disposal, deep-sea exploration, and volcanic activity studies, etc.) will be powered purely by algorithms. 

Lastly, machines are expected to analyze and understand colossal volumes of data. According to Stuart Russell, The combination of AI-powered algorithms and free access to Big Data can identify new, unexpected patterns we’ll be able to use to mathematically predict future events or solve global challenges like climate change. 

What a time to be alive! 

If you wish to learn more about Artificial Intelligence technologies and applications, and want to pursue a career in the same, upskill with Great Learning’s PG course in Artificial Intelligence and Machine Learning.

 

Critical skill-sets to make or break a data scientist 

Ever since data took over the corporate world, data scientists have been in demand. What further increases the attractiveness of this job is the shortage of skilled experts. Companies are willing to pour their revenue into the pockets of data scientists who have the right skills to put an organization’s data at work.

However, that does not mean it is easy for candidates to grab a job at renowned organizations. If you’ve been wanting to establish a career in data science, know that it takes the right set of skills to be considered worthy of the position.

What exactly then do you need to become an in-demand data scientist?

Here are a few valuable skills required for data scientist to inculcate before hitting the marketplace looking for your ideal job.

Programming or Software Development Skills

Data scientists need to toy with several programming languages and software packages. They need to use multiple software to extract, clean, analyze, and visualize data. Therefore, an aspiring data scientist needs to be well-versed with:

– Python – Python was not formally designed for data science. But, now that data analytics and processing libraries have been developed for Python, giants such as Facebook and Bank of America are using the language to further their data science journeys. This high-level programming language is powerful, friendly, open-source, easy to learn, and fast.

– R – R was once used exclusively for academic purposes, but a number of financial institutions, social networking services, and media outlets now use this language for statistical analysis, predictive modelling, and data visualization. This is a reason why R is important for aspiring data scientists to get their hands on.

– SQL – Structured Query Language is a special-purpose language that helps manage data in relational database systems. SQL helps you in inserting, querying, updating, deleting, and modifying data held in database systems. 

– Hadoop – This is an open-source framework that allows distributed processing of large sets of data across computer clusters using simple programming models. Hadoop offers fault tolerance, computing power, flexibility, and scalability in processing data.

Problem Solving and Risk Analysis Skills

Data scientists need to maintain exceptional problem-solving skills. Organizations hire data scientists to work on real challenges and attempt to solve them with data and analytics. This needs an appetite to solve real-world problems and cope with complex situations. 

Additionally, aspiring data scientists also need to be a master at the art of calculating the risks associated with specific business models. Since you will be responsible for designing and installing new business models, you will also be in charge of assessing the risks that entail them. 

skills required for data scientist
Summary of critical skills required for data scientists

Process Improvement Skills

Most of the data science jobs in this era of digital transformation have to deal with improving legacy processes. As organizations move closer to transformation, they need data scientists to help them replace traditional with modern.

As a data scientist, it falls upon you to find out the best solution to a business problem and improve relevant processes or optimize them. 

It makes a lot of sense for data scientists to develop a personalized approach to improving processes. If you can show your potential employer that you can enhance their current business processes, you will significantly increase your chances of landing the job.

Mathematical Skills

Unlike many high-paying jobs in computer science, data science jobs need both practical and theoretical understanding of complex mathematical subjects. Here are a few skills you need to master under this set:

– Statistics – No points for guessing this one, but statistics is and will be one of the top data science skills for you to master. This branch of mathematics deals with the collection, analysis, organization, and interpretation of data. Among the vast range of topics you might have to deal with, you’ll need a strong grasp over probability distributions, statistical features, over and undersampling, Bayesian statistics, and dimensionality reduction. 

– Multivariable calculus and linear algebra – Without these technologies, it is hard to curate the modern-day business solutions. Linear algebra happens to be the language of computer algorithms, while multivariable calculus is the same for optimization problems. As a data scientist, you will be tasked with optimizing large-scale data and defining solutions for them in terms of programming languages. Therefore, it is essential for you to have a stronghold over these concepts.

Deep Learning, Machine Learning, Artificial Intelligence Skills

Did you know, as per PayScale, the data scientists equipped with the knowledge of AI/ML get paid up to INR 20,00,000 with an average of INR 7,00,000? Modern-day businesses need their data scientists to have a basic understanding, if not expertise, over these technologies. Since these areas of technology have to do a lot with data, it makes sense for you to have a foundational understanding of these concepts.

Learning the ins and outs of these concepts will highly increase your data science skills and help you stand out from other prospective employees.

Collaborative Skills

It is highly unlikely for a data scientist to work in solitude. Most companies today house a team of data science experts who work on specific classes of problems together. Even if not in a team of data scientists, you will definitely need to collaborate with business leaders and executives, software developers, and sales strategists among others.

Therefore, when putting all of the necessary skills in perspective, do not forget to inculcate teamwork and collaborative skills. Define the right ways of bringing issues in front of people and explaining your POV without exerting dominance.

It might also help you to be able to explain data science concepts and terminologies in a simple language to non-experts.

For the year 2019, the total number of analytics and data science job positions available are 97,000, which is more than 45% as compared to the last year. Trends like this act as a magnet to attract fresh graduates towards a career in Data Science. As a data scientist, you need to wear multiple hats and ace them all. Since the field is currently expanding and evolving, it is hard to predict everything that a data scientist needs to know. However, start by working on these preliminary skills required for data scientist and then move your way up.

If you are interested in moving ahead with a career in Data Science, then you should start inculcating the above-mentioned skills to improve your employability. Upskilling with Great Learning’s PG program in Data Science Engineering will do the most of it for you!

Artificial Intelligence Weekly Round-up: July 9, 2019

Here are a few Artificial Intelligence updates from last week to keep you informed.

Indeed’s 2019 Report of Top 10 AI Jobs and Highest Salaries is Finally Out!

Like every year, Indeed published a report analyzing the tech industry’s top artificial intelligence (AI) jobs and highest salaries. There was a considerable increase (29%) in the number of AI jobs as compared to last year’s report.

How AI and Machine Learning Helps in Up Skilling to Better Career Opportunities

AI will create nearly 2.3 million jobs by next year. Nearly all forms of enterprise software, factory automation, transport, and other industries are increasingly using AI-based interfaces in their daily operation. In fact, by 2030, AI may end up offering USD 15.7 trillion to the global economy…. [Read More]

5 Industries that Heavily Rely on Artificial Intelligence and Machine Learning

Machine Learning and Artificial Intelligence are pushing every industry towards precise business analysis and optimizing operations. Here are 5 industries that heavily rely on AL and ML technologies to grow and perform better…. [Read More]

10 Breakthrough Technologies 2019, Curated by Bill Gates

Here is a list of 10 breakthrough technologies, that as per Bill Gates, will rule the year 2019…. [Read More]

Artificial Intelligence, the Future of Work, and Inequality?

With Artificial Intelligence coming into play many jobs will be displaced and employees will relocate to different jobs. For low- and medium skill workers, it is likely that the relocation will occur in the lower rung of jobs, meaning either lower pay or fewer benefits. Workers who possess skills that are complementary to new technologies will benefit in the form of higher wages. Hence, citizens and policymakers concerned with the rise of automation should focus on its effects on inequality, and upskilling could be a solution to this… [Read More]

Happy Reading!

 

 

 

 

Basics of building an Artificial Intelligence Chatbot

Chatbots are not a recent development. The first chatbot was created by Joseph Wiesenbaum in 1966, named Eliza. It all started when Alan Turing published an article named “Computer Machinery and Intelligence”, and raised an intriguing question, “Can Machines think?”, and ever since, we have seen multiple chatbots surpassing their predecessors to be more naturally conversant and technologically advanced. These advancements have led us to an era where conversations with chatbots have become as normal and natural as with another human.

  

Today, almost all companies have chatbots to engage their users and serve customers by catering to their queries. As per a report by Gartner, Chatbots will be handling 85% of the customer service interactions by the year 2020. Also, 80% of businesses are expected to have some sort of chatbot automation by 2020 (Outgrow, 2018). We practically will have chatbots everywhere, but this doesn’t necessarily mean that all will be well-functioning. The challenge here is not to develop a chatbot, but to develop a well functioning one. 

Let’s have a look at the basics of creating an Artificial Intelligence chatbot:

Identifying opportunity for an Artificial Intelligence chatbot

The first step is to identify the opportunity or the challenge to decide on the purpose and utility of the chatbot. To understand the best application of Bot to the company framework, you will have to think about the tasks that can be automated and augmented through Artificial Intelligence Solutions. For each type of activity, the respective artificial intelligence solution broadly falls under two categories: “Data Complexity” or “Work Complexity”. These two categories can be further broken down to 4 analytics models namely, Efficiency, Expert, Effectiveness, and Innovation.

Understanding Customer Goals

There needs to be a good understanding of why the client wants to have a chatbot, and what the users and customers want their chatbot to do. Though it sounds very obvious and basic, this is a step that tends to get overlooked frequently. One way is to ask probing questions so that you gain a holistic understanding of the client’s problem statement. This might be a stage where you discover that a chatbot is not required, and just an email auto-responder would do.. In cases where client itself is not clear regarding the requirement, ask questions to understand specific pain points and suggest most relevant solutions. Having this clarity helps the developer to create genuine and meaningful conversations to ensure meeting end goals.

Designing a chatbot conversation

There is no common way forward for all different types of purposes that chatbots solve. Designing a bot conversation should depend on the purpose the bot will be solving. Chatbot interactions are categorized to be structured and unstructured conversations. The structured interactions include menus, forms, options to lead the chat forward, and a logical flow. On the other hand, the unstructured interactions follow freestyle plain text. This unstructured type is more suited to informal conversations with friends, families, colleagues and other acquaintances. 

Selecting conversation topics is also critical. It is imperative to choose topics that are related to and are close to the purpose served by the chatbot. Interpreting user answers, and attending to both open-ended and close-ended conversations are other important aspects of developing the conversation script. 

Building a chatbot using code-based frameworks or chatbot platforms

There is no better way among the two to create a chatbot. While the code-based frameworks provide flexibility to store-data, incorporate AI, and produce analytics, the chatbot platforms save time and effort and provide highly functional bots that fit the bill.

Some of the efficient chatbot platforms are:

Chatfuel — the standout feature is broadcasting updates and the content modules to automatically to the followers. Users can request information and converse with the bot through predefined buttons, or information could be gathered inside messenger through ‘Typeform’ style inputs.

Botsify — User-friendly drag and drop templates to create bots. Easy integration to external plugins and various AI and ML features help improve the conversation quality and analytics. 

Flow XO —  This platform has more than 100+ integrations and the easiest to use the visual editor. But, it is quite limited when it comes to AI functionality.

Beep Boop — Easiest and best platform to create slack bots. Provides an end to end developer experience. 

Bottr — There is an option to add data from Medium, Wikipedia, or WordPress for better coverage. This platform gives an option to embed a bot on the website.

For the ones who are more tech-savvy, there are code-based frameworks that would integrate the chatbot into a broader tech stack. The benefits are flexibility to store data, provide analytics, and incorporate Artificial Intelligence in the form of open source libraries and NLP tools.

Microsoft Bot Framework — Developers can kick off with various templates such as basic, language understanding, Q&As, forms, and more proactive bots. It is the Azure bot service which and provides an integrated environment with connectors to other SDKs. 

Wit.AI (Facebook Bot Engine) — This framework provides an open natural language platform to build devices or applications that one can talk or text. It learns human language from the interactions and shares this learning to leverage the community. 

API.AI (Google Dialogflow) — This framework also provides AI-powered text and voice-based interaction interfaces. It can connect with users on Google Assistant, Amazon Alexa, Facebook Messenger, etc.

Testing your chatbot

The final and most crucial step is to test the chatbot for its intended purpose. Even though it’s not important to pass the Turing Test first time around, it still must be fit for the purpose.

Test the bot with a set of 10 beta testers. The conversations generated will help in identifying gaps or dead-ends in the communication flow. 

With each new question asked, the bot is being trained to create new modules and linkages to cover 80% of the questions in a domain or a given scenario. By leveraging the AI features in the framework the bot will get better each time.

If you wish to learn more about Artificial Intelligence technologies and applications, and want to pursue a career in the same, upskill with Great Learning’s PG course in Artificial Intelligence and Machine Learning.