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Microsoft Introduces FPGA Technology atop Google Chips through Project Brainwave

Posted on May 10, 2018May 23, 2020 by Dexlab

A Change Is In the Make – due to increasing competition among tech companies working on AI, several software makers are inventing their own new hardware. A few Google servers also include chips designed for machine learning, known as TPUs exclusively developed in-house to ensure higher power and better efficiency. Google rents them out to its cloud-computing consumers. Of late, Facebook too shared its interest in designing similar chips for its own data centers.

However, a big player in AI world, Microsoft is skeptical if the money spent is for good – it says the technology of machine learning is transforming so rapidly that it makes little sense to spend millions of dollars into developing silicon chips, which could soon become obsolete. Instead, Microsoft professionals are pitching for the idea of implementing AI-inspired projects, named FPGAs, which can be re-modified or reprogrammed to support latest forms of software developments in the technology domain. The company is buying FPGAs from chip mogul, Intel, and already a few companies have started buying this very idea of Microsoft.

This week, Microsoft is back in action with the launch of a new cloud service for image-recognition projects, known as Project Brainwave. Powered by the very FPGA technology, it’s one of the first applications that Nestle health division is set to use to analyze the acuteness of acne, from images submitted by the patients. The specialty of Project Brainwave is the manner in which the images are processed – the process is quick as well as very low in cost than other graphic chip technologies used today.

It’s been said, customers using Project Brainwave are able to process a million images in just 1.8 milliseconds using a normal image recognition model for a mere 21 cents. Yes! You heard it right. Even the company claims that it performs better than it’s tailing rivals in cloud service, but unless the outsiders get a chance to test the new technology head-to-head against the other options, nothing concrete can be said about Microsoft’s technology. The biggest competitors of Microsoft in cloud-service platform include Google’s TPUs and graphic chips from Nvidia.

At this stage, it’s also unclear how widely Brainwave is applicable in reality – FPGAs are yet to be used in cloud computing on a wide scale, hence most companies lack the expertise to program them. On the other hand, Nvidia is not sitting quietly while its contemporaries are break opening newer ideas in machine learning domain. The recent upgrades from the company lead us to a whole new world of specialized AI chips that would be more powerful than former graphic chips.

Latest reports also confirm that Google’s TPUs exhibited similar robust performance similar to Nvidia’s cutting edge chips for image recognition task, backed by cost benefits. The software running on TPUs is both faster and cheaper as compared to Nvidia chips.

In conclusion, companies are deploying machine learning technology in all areas of life, and the competition to invent better AI algorithms is likely to intensify manifold. In the coming days, several notable companies, big or small are expected to follow the footsteps of Microsoft.

For more machine learning related stories and feeds, follow DexLab Analytics. It is the best data analytics training institute in Gurgaon offering state of the art machine learning using python courses.

The article has been sourced from – https://www.wired.com/story/microsoft-charts-its-own-path-on-artificial-intelligence

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Python Machine Learning is the Ideal Way to Build a Recommendation System: Know Why

Posted on May 9, 2018May 9, 2018 by Dexlab

In recent years, recommendation systems have become very popular. Internet giants, like Google, Facebook and Amazon, use algorithms to tailor search results to customer preferences. Any system that has a search bar collects data on a customer’s past behavior and likings, which enable these platforms to provide relevant search results.

All businesses need to analyze data to give personalized recommendations. Hence, developers and data scientists are investing all their energies and mental faculties to come up with perfect recommendation systems. Many of them are of the opinion that Python Machine Learning is the best way to achieve this. Often, building a good recommendation system is considered as a ‘rite of passage’ for becoming a good data scientist!

Delving into recommendation systems:

The first step in the process of building a recommendation system is choosing its type. They are classified into the following types:

Recommendation based on popularity:

This is a simplistic approach, which involves recommending items that are liked by the maximum number of users. The drawback of this approach is its complete exclusion of any personalization techniques. This approach is extensively used in online news portals. But in general, it isn’t a popular choice for websites because it bases popularity on entire user pool, and this popular item is shown to everyone, irrespective of personal choice and interest.

Recommendation based on algorithms:

This process uses special algorithms that are tailor-made to suit every customer. They are of two types:

Content based algorithms:

These algorithms are based on the idea that if a person likes a product then he/she will also like a similar product. It works efficiently when it is possible to determine the properties of each product. It is used in movie and music recommendations.

Collaborative filtering algorithms:

These algorithms are dependent on past behavior and not on properties of an item. For example, if a person X likes items a, b, c and another person Y likes items b, c, d, then it is concluded that they have similar interests and X should like item d and Y should like item a. Because they are not dependent on additional information, collaborative filtering algorithms are very popular. E-commerce giants, like Amazon and Flipkart, recommend products based on these algorithms.

After choosing the type of recommendation system to build, developers need to locate relevant datasets to apply to it. The next step is determining the platform where you’ll build your recommendation system. Python machine learning is the preferred platform.

Advantages of using Python Machine Learning:

Code: Python makes the process of writing code extremely easy and working with algorithms becomes quite convenient. The flexible nature of this language and its efficiency in merging different types of data sets make it a popular choice for application in new operating systems.
Libraries: Python encompasses a wide range of libraries in multiple subjects, such as machine learning and scientific computing. The availability of a large number of functions and methods enables users to carry out several actions without having to write their own codes.
Community: Python includes a large community of young, bright, ambitious and helpful programmers. They are more than willing to provide their valuable inputs on different projects.
Open source: The best part about Python is that it is completely open source and has sufficient material available online that will help a person develop skills and learn essential tips and tricks.

Proficiency in Python is highly advantageous for anyone who wants to build a career in the field of data science. Not only does it come handy in building complicated recommendation systems, it can also be applied to many other projects. Owing to its simplicity, Python Machine Learning is a good first step for anyone who is interested in gaining knowledge of AI.

In the current data-driven world, knowing Python is a very valuable skill. If one’s aim is to collect and manipulate data in a simple and efficient manner, without having to deal with complicated codes, then Python is the standard.

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How is AI Shaping the Indian Job Market?

Posted on May 7, 2018May 23, 2020 by Dexlab

Currently, startups focusing on Artificial Intelligence, Machine Learning and Deep Learning are on the rise in India. According to a recent report by AI Task Force, there are 750 startups in India that are actively working to build a robust AI ecosystem in India. Initiatives to promote AI by Indian government include establishment of NITI Aayog, the policy think tank of India, and Digital India, which is a campaign to improve technological infrastructure of the country.

65% of participants of a PwC survey believed that AI will have a grave impact on the employment scenario of India. Interestingly, the majority of participants of this survey were of the opinion that AI will allow employees to do more value-added tasks as it will take up all the daily mundane tasks.

Job market outlook:

‘’We expect a 60 per cent increase in demand for AI and machine learning specialists in 2018’’, said BN Thammaiah, Managing Director, Kelly Services India. Belong, a Bengaluru-based outbound hiring firm startup, shares the same view, stating that the demand for AI professionals has risen by leaps and bounds due to the widespread adoption of AI and automation technologies across companies. Consulting industry leader, Accenture, expects AI to add $957 to India’s GDP by 2035.

Jump in demand:

Only 4 percent of AI professionals have work experience in core domains, like deep learning and neural networks.

For every 1000 jobs in the field of Deep learning, there are approximately 530 professionals available. Similarly, for every 1000 jobs in the field of Neuro-linguistic Programming (NLP), there are only 710 professionals available.

The lack of core data science disciplines in engineering institutes across the country is responsible for the disparity between demand and supply of AI professionals. Only a few selected institutes, like IITs and IISc, have ML programs in their curriculum. The active AI researchers in India are a meager 386 in number.

AI hotspots in India:

AI-work hubs in India are Bengaluru, New Delhi and Mumbai. IBM, Microsoft, Flipkart and Amazon are carrying out good research work in AI. Companies like Adobe, Accenture, Amazon, JP Morgan, SAP, L&T Infotech, Nvidia, Intel and Wipro are actively hiring AI professionals. The main sectors fostering AI employment are e-commerce, banking and finance. Kamal Karanath, Co-founder of Xpheno, a recruitment company, said that there would be a huge demand for AI engineers in these sectors in the next 5 years. AI-powered technology boosts efficiency and security of Indian banking and financial sector.

India Inc is endeavoring to upskill workers in subjects like machine learning, cloud computing and big data. In efforts to nurture talent and obtain solutions from vertical focused AI startups, which are developing innovative technologies, enterprises have set up many accelerator programs. Flipkart is developing AI products that will boost their business growth.

A peek into the future of AI:

The Indian government intends to establish research institutes and Centres of Excellence that foster training and skilling in fields like AI, robotics, big data analysis and internet of things. Top engineering schools, like IITs, IIITs and IISc are collaborating with industries to bridge the gap in AI talent, provide targeted solutions and steer growth of the AI industry. Government of India is framing numerous policies to promote industry-academic partnerships.

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How Conversational AI and Chatbots are Revolutionizing Indian banking Industry

Posted on April 25, 2018December 3, 2018 by Dexlab

Thanks to the advancements in AI and ML, bank work can now be done with the click of a phone button! Innovations in the field of customer services form an important part of the technology overhaul. The banking sector is making hefty investments on AI technology to simplify user experience and enhance overall performance of financial institutes.

Let’s take a look at how conversational AI and chatbots are revolutionizing the Indian banking industry.

Keya by Kotak Mahindra Bank

Keya is the first AI-powered chatbot in Indian banking sector. It is incorporated in Kotak’s phone-banking helpline to improve its long-established interactive voice response (IVR) system.

‘’Voice commands form a significant share of search online. In addition, the nature of the call is changing with customers using voice as an escalation channel. Keya is an intelligent voicebot developed keeping in mind the customers’ changing preference for voice over text. It is built on a technology that understands a customer’s query and steers the conversation to provide a quick and relevant response”, says Puneet Kapoor, Senior Executive Vice President, Kotak Mahindra Bank.

Bank of Baroda chatbot

Akhil Handa, Head of Fintech Initiatives, Bank of Baroda said that their chatbot will manage product-related queries. He believes that the services of the chatbot will result in better customer satisfaction, speedy responses and cost minimization.

Citi Union Bank’s Lakshmi Bot

Lakshmi, India’s first humanoid banker is a responsive robot powered by AI. It can converse with customers on more than 125 topics, including balance, interest rates and transactional history.

IBM Watson by SBI

Digital platforms of SBI, like SBI inTouch, are utilizing AI-powered bots, such as IBM Watson, to enhance customer experience. SBI stated that modern times will witness the coexistence of men and machines in banks.

AI-driven digital initiatives by YES Bank in partnership with Payjo

Payjo is a top AI Banking platform based out of Silicon Valley in California. YES Bank has partnered with Payjo to launch YES Pay Bot, its first Bot using AI, which improves already popular wallet services. The YES Pay wallet service is trusted by more than half-a-million customers.

YES TAG chatbot

YES TAG chatbot has been launched by YES Bank and enables transactions through 5 messaging apps. Customers can carry out a wide range of activities, such as check balance, FD details, status of cheque, transfer money, etc. It is currently used in Android and will soon be available on Apple App Store.

Digibank

Asia’s largest bank, DBS Bank, has developed Digibank, which is India’s first mobile bank that is ‘chatbot staffed’. It provides real-time solution to banking related issues. This chatbot employs a trained AI platform, called KAI, which is a product of New York startup- Kasisto.

Axis Bank launches intelligent chatbot in association with Active.ai

Axis Bank facilitates smart banking with the launch of a chatbot that employs conversational interface to offer interactive mobile banking solutions. This intelligent chatbot was developed in association with Singapore based AI company- Active AI.

HDFC Bank launches OnChat in partnership with Niki.ai

To enable smooth ecommerce and banking transactions, HDFC in partnership with Niki.ai has launched a conversational chatbot, called OnChat. It is available on Facebook messenger even to people who aren’t HDFC customers. Users can recharge phone, book cabs and pay utility bills through this chatbot.

EVA by HDFC Bank

EVA is exclusively for the customers of HDFC Bank. It is an electronic virtual assistant developed in partnership with Senseforth, an AI startup based in Bengaluru.

mPower by YES Bank

mPower is a chatbot for loan products that has been developed by YES Bank in association with Gupshup-a leading bot company. It assists customers on a variety of loan related topics like personal loans, car loans and loan against securities.

In the future, there will be three kinds of bots- speech-based bot, textbots and video chatbots. Conversational bots work in harmony with human employees to enrich customer experience.

Thus, AI-powered technology is the way forward. To be industry-ready in this AI-era, enroll for the Machine Learning course in Gurgaon at Dexlab Analytics. It is a premier Analytics training institute in Delhi.

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How Artificial Intelligence is Boosting the Design of Smarter Bionics

Posted on April 10, 2018May 23, 2020 by Dexlab

Artificial Intelligence and Machine Learning are a set of technologies that empower machines and computers to learn and evolve upon their own learning through constant reiteration and consistent data bank upgrades. The entire chain of mechanism stands on recursive experiments and human intervention.

Bionics

Advances in technology have greatly benefited the field of prosthetics in the last few years. Today’s prosthetic limbs are made using space-age materials that provide increased durability and function. In addition, many prosthetics make use of bionic technology. These types of prosthetics are called myoelectric prosthetics.

Myoelectric prosthetics picks up the electrical action potential from the residual muscles in the amputated limb. Upon receiving the action potentials, the prosthetic amplifies the signal using a rechargeable battery. Detected signal can be used further into usable information to drive a control system. Artificial Intelligence helps to identify motion commands for the control of a prosthetic arm by evidence accumulation. It is able to accommodate inter-individual differences and requires little computing time in the pattern recognition. This allows more freedom and doesn’t require the person to perform frequent, strenuous muscle contractions. The inclusion of AI technology in prosthetics has helped thousands of amputees return to daily activities. While technologies that make bionic implants possible are still in infancy stage, many bionic items already exist.

The Bionic Woman

Angel Giuffria is an amputee actress and Ottobock Healthcare’s Brand Champion who has been wearing electromechanical devices since she was four months old. Following are excerpts of an interview with her.

“I wear currently the bebionic 3 small-size hand which sounds like a car. But at this point, that’s where we’re getting with technology. It’s a multi-articulating device. That small-size hand is really amazing… this technology wasn’t available to them previously”

She further added, “..The new designs that look more tech are able to showcase the technology. I’ve really become attached to and I think a lot of other people have really clung onto as well because it just gives off the impression of showing people how capable we are in society now.”

She also spoke about prosthetics like the Michelangelo Hand which is stronger, faster and has multiple hook options. Modern additions to prosthetics such as lights and cameras are added advantages. She describes her hand to be able to do multiple functions like change grip patterns and control wrist movements which enable her to hold small items like keys and credit cards.

I-limb

Bertolt Meyer’s amazing bionic hand controlled via an iPhone app is another glimpse at the advances being made in prosthetics.

In 2009, Meyer, a social psychologist at the University of Zurich was fitted with an i-limb, a state-of-the-art bionic prosthesis developed by a Scottish company, Touch Bionics, which comes with an aluminum chassis and 24 different grip patterns. To select a new suite of gestures, Meyer simply taps an app on his iPhone. He describes his i-limb to be the first, where aesthetics match engineering.

In the world of prosthetics, function is the key. Most amputees are constantly searching for the same level of functionality that they enjoyed before they lost their limb. With the introduction of artificial intelligence in prosthetic limbs, amputees are closer to their goals than ever before. Bionics having access to the relevant databases are capable of learning new things in a programmed manner which improves their performance.

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How Machine Learning and Sensors Help Detect Cyber Threats within Power Distribution Networks

Posted on April 5, 2018April 5, 2018 by Dexlab

Machine learning in marriage with cutting edge sensor technology helps alpha geeks detect and assess cyber-physical attacks across power-distribution channels.

Today, losing power and imagining a life without technology sounds unreal. It’s more than just an inconvenience. The truth is we rely on electricity much more than we even do realize. Even though you are not a techie or someone belonging from the IT domain, you still stay dependent on electricity and power. They have become the BASICS.

For this reason or more, power companies have initiated a ‘deep dependency’ concept, Smart Grid – it’s an effective and powerful power-distribution structure. It’s originally a power-line internet that harbors exceptional capabilities within.

Machine Learning and Sensors to Ensure Security to Power Grids

A team of eminent researchers are toiling rigorously to integrate machine-learning algorithms, cybersecurity methodology and commercially-available power-system sensor technology into a security monitoring and analysis framework to support power grids.

The team is at present working on the framework’s architecture for detection of cyber-physical attacks on any power-distribution network. “To do this they are using micro-Phasor Measurement Units (µPMUs) to capture information about the physical state of the power distribution grid,” explains Kathy Kincade, Lawrence Berkeley National Laboratory. “They then combine this data with SCADA (Supervisory Control and Data Acquisition) information to provide real-time feedback about system performance.”

Note: Kathy Kincade published a Lawrence Berkeley National Laboratory press release: Combination of Old and New Yields Novel Power Grid Cybersecurity Tool, which talks elaborately on this issue.

The notion here is to keep a close watch on the physical behavior of the components within a particular electric grid to understand when devices are under attack, how they are manipulated weirdly. These devices act as a redundant set of measurements that offers veritable ways to monitor everything that’s going within a power distribution grid.

One of the researchers, Sean Peisert (Berkeley Labs) articulates the importance of redundant measurements permitted by implementing both µPMU and SCADA devices. He further says, “Individually it might be possible for an attacker to manipulate what is being represented by any single sensor or source of information, which could lead to damage of the power grid. This approach provides the redundancy and therefore resilience in the view that is available to grid operators.”

System redundancy comes with an additional benefit of distinguishing real attacks from false alarms by comparing µPMU measurements to what the device reports.

An Algorithm for Real-time Reporting

The proud researchers formulated an algorithm in 1954 for their machine learning endeavors. The algorithm aids software in identifying if measurements like active power, reactive power and current magnitude are normal or abnormal by discerning robust changes across the physical environment.

The Last Thoughts

Cyber attacks are becoming increasingly widespread. Every other day, you might find some headlines or tech page news surfacing out, intensifying how cyber attacks are plaguing our lives, digitally. Therefore, it’s high time to learn from the pundits how to work on the issue.

As Peisert concludes, “Using high-resolution sensors in the power-distribution grid and a set of machine-learning algorithms that we developed, in conjunction with a simple model of the distribution grid, our work can be deployed by utilities in their distribution grid to detect cyberattacks and other types of failures,” it stresses on the significance of machine learning algorithms to combat such attacks.

The original article first appeared in – https://www.techrepublic.com/article/power-grid-cybersecurity-tool-uses-machine-learning-and-sensors-to-detect-threats

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Here’s Why Automation Is Gaining Accolades All over the World?

Posted on March 28, 2018December 31, 2018 by Dexlab

Over the last few decades and more, if there’s anything that has continuously evolved, surprising us with something new at each turn, it’s TECHNOLOGY. Technological advancements have come to a point where people and businesses have started relying on Automation.

Automation is the technology by which a process is performed entirely by means of various control systems and equipments without human assistance. The purpose is to enhance productivity, deliver faster results and save costs, depending on the industry and the extent to which automation is being applied.

Automation, which is mostly about streamlining the work process, can take place either by means of implementing intelligence into the existing systems or by replacing the same. Some of these systems operate without human interference while others support humans and pave ways for better productivity.

Leveraging Automation for Good

How to introduce automation into the workflow and the procedure of doing that has been a matter of concern for most entrepreneurs. Putting machines to work requires certain considerations. Following are a few smart tips for introducing automation to your business and leveraging the most out of it.

Putting the machines to work for you necessitates you to implement the automation tools gradually. This should always be followed by a close evaluation of the existing process so that it is applied to where streamlining is needed the most.
Automation is an instant success when you start things manually, but sometimes things can take a downturn if you lack enough knowledge of the domain. The switch to automation should be planned ahead with care.
Automation is a change that most people will be reluctant to adopt at least initially. So, it is always better to ensure that there is a solid change management strategy in place to make the technology actually make lives easier and not otherwise.

Automation isn’t something NEW ON THE BLOCK

The very notion of automation has been in the minds of entrepreneurs as well as traditionalists for around centuries. For an example, an elevator that’s been in existence for so many years is an automation tool for mobility. Recently, massive breakthroughs have been achieved in this budding field, and cloud computing is one of them. It has driven the cost of storage solutions really down, while pushing machine learning skills to a new high.

In short, while automation has always been more of a IT tool, it’s now finding its footing in other domains within an organization. “Whether it’s a catalog or an app, it now allows me as a consumer to control things when they’re happening,” says Marc Wilkinson, CTO for Workplace and Mobility at DXC.

Automation and employment crunch

As it’s said, automation isn’t about eliminating jobs instead it’s more about creating efficiencies. If the work done in twice the speed, it would be more efficient and employer will benefit from productivity gains and the employees in the event will hit off high-impact projects.

So, in a sense, automation won’t entirely result in lower employee headcount, though it may affect a certain kind of employment. High-touch occupations will witness limited impact. For an instance, hospitals have to very much human-to-human; they can’t go totally automized.

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Quantum Computing Going Commercial: IBM and Google Leading the Trail

Posted on March 22, 2018March 22, 2018 by Dexlab

Quantum computing is all set to make a debut in the commercial world – tech bigwigs, like IBM and Google are making an attempt to commercialize quantum computing. Julian Kelly, a top notch research scientist at Google’s Quantum AI Lab announced with a joint collaboration with Bristlecone, a quantum processor that offers a testbed for various research activities on quantum technology and machine learning, quantum supremacy can be achieved and this could be a great stepping stone for building larger scale quantum computers.

After Google, IBM is also making significant progress in commercializing quantum computing technology by taking it to the cloud in 2016 with a 5 qubit quantum computer. Also, last year, November they raised the bar by declaring that they are going to launch third generation quantum computer equipped with a 50 quibit prototype, but they were not sure if it will be launched on commercial platforms, as well. However, they created another 20 qubit system available on its cloud computing platform.

Reasons Behind Making Quantum Computing Commercialized:

Might lead to fourth industrial revolution

Quantum computing has seeped in to an engineering development phase from just a mere theoretical research – with significant technological power and constant R&D efforts it can develop the ability to trigger a fourth industrial revolution.

Beyond classic computing technology

Areas where conventional computers fail to work, quantum computing will instill a profound impact – such as in industrial processes where innovative steps in machine learning or novel cryptography are involved.

Higher revenue

Revenues from quantum computing are expected to increase from US$1.9 billion in 2023 to US$8.0 billion by 2027 – as forecasted by Communications Industry Researchers (CIR).

Market expansion

The scopes of quantum computing have broadened beyond expectations – it has expanded to drug discovery, health care, power and energy, financial services and aerospace industry.

From cloud to on-premise quantum technology

To incorporate quantum computing into the heart of the business operations’ computing strategy, the companies are contemplating to add a new stream of revenue by implementing quantum computing via cloud. In the future, it’s expected to see a rise in on-premise quantum computing – because the technology is already gaining a lot of accolades.

Better growth forecasts

In the current scenario, the quantum enterprise market is still at a nascent stage with a large user base in the R&D space. But by 2024, it has been forecasted that this share would be somewhere around 30% and the powerful revenue drivers will be industries, like defense, banking, aerospace, pharmaceutical and chemical.

IBM or Google? Who is a clear winner?

In the race to win quantum supremacy, IBM is a sure winner and has made stunning progress in this arena, even though it is receiving stiff competition by Google recently. Google’s new quantum processor Bristlecone has the ability to become a “compelling proof-of-principle for building larger scale quantum computers”. For this, Julian Kelly suggested, “operating a device such as Bristlecone at low system error requires harmony between a full stack of technology ranging from software and control electronics to the processor itself. Getting this right requires careful systems engineering over several iterations.”

As last notes, quantum computing has come out from being a fundamental scientific research to a structural engineering concept. Follow a full-stack approach, coupled with rapid testing and innovative practices and establish winning control over this future tool of success.

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The article has been sourced from – https://analyticsindiamag.com/why-are-big-tech-giants-like-google-ibm-rushing-to-commercialize-quantum-computing

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How Algorithms Shape Public Discourse and Opinions?

Posted on March 14, 2018May 23, 2020 by Dexlab

The rapid evolution of today’s communication mediums has brought about a radical change in how public opinions are framed and public discourse is conducted. In general, the conventional boundary between public and personal communication has somewhat disappeared.

Incredible global platforms, like Google allow us all gain access to information in a blink of an eye. In order to do so, they use computer algorithms that weigh “relevance”, but sometimes the standards do not correspond to the expectation of the users.

Custom-fit Relevance

Algorithms function distinctly and descriptively. For an instance, the technology alters “relevance” for a user based on what links he or she has clicked in the past. But still, many users think the results are normative (‘higher’ up in Google results). In several cases, Google’s algorithms determine a massive divergence between content quality and “relevance”.

Not only that, owing to their encompassing database, Google and Facebook play a strong role in forming public opinions. 57% of German internet users get access to information about social affairs and politics through Google and other social networks. The researchers from Hamburg-based Hans Bredow Institute quoted in 2016, “the formation of public opinion is no longer conceivable without intermediaries, such as Google and Facebook”.

Keep Engaged

The design elements that Google and other intermediaries use are leading to a structural change in public discourse. Today, publishing is a piece of cake. Anyone can publish anything on the web, but everyone might not find an audience – for the latter, decision-making algorithms are needed. They garner the needed attention. They also determine the relevance of each content piece that goes through various social networks, like Facebook and filter the items that should be displayed for each user. In making an individual’s feed attractive, Facebook runs a detailed analysis and determines which content the user and his or her friends’ likes or prefers to hide. Both signals are important to perform a fairly straightforward analysis.

Moreover, Facebook deploys signals that users have no idea about, such as the amount of time they take to look into a single entry in the feed. In other areas too, algorithmic decision-making plays a crucial role, like offering help in legal matters or assessing where and when the police officers are on duty.

Diversity Rules

To guarantee a diversity of media in the public, make sure the algorithmic decision-making processes that determine relevance are diverse in the same manner. The digital discourse is supported by the robust algorithms that constantly ranks and personalize content.

To instill transparency to algorithmic decision-making methodologies, follow the steps below:

Back external researchers, and open platforms and their impacts
Support diversity among algorithms
Develop and maintain a strong code of ethics among developers
Educate users about the importance of the mechanisms used to influence public opinions

Coupled with strong industry self-regulation and legislative measures, a true and impartial notion of social and political influences on algorithmic ranking is established, which carries the potential to discover and combat dangers early on.

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