How Facebook and Google are pushing Mobile UX to its limits

In an endeavor to ensure lesser loading times for news and media web pages across the mobile web, Facebook and Google came up with Instant Articles and Accelerated Mobile Pages (AMP).

For Facebook, the initiative was focused on keeping users from leaving the social-media channel rather than referring traffic to online publishers. For Google, the project focused on building lightweight web pages by using an open-source AMP HTML code framework. In both cases, the focus was on radically improving the mobile user experience.

How did ‘Facebook’s Instant Articles’ Improve its Mobile UX?

With Instant Articles, publishers can now host their stories and posts on the Facebook servers, which proved beneficial in loading linked articles ten times quicker than a separate web app or page. With various interactive tools like auto-play videos, maps, zooming, comments, audio captions, analytics tools, and others, the project lends handy tools for publishers and a great mobile user experience.

With speed being the main selling point, Instant Articles also ensured visual consistency and readability by having good visual design standards and lesser visual clutter.

Publications that already signed up for Instant Articles were National Geographic, The New York Times, BBC News, Fox Sports, The Washington Post, The Onion, The Huffington Post, The Verge, The Atlantic, Business Insider, TIME, Hollywood Reporter, and others.

How will ‘Google’s Accelerated Mobile Pages’ Enhance Mobile UX?

As per Google, with AMP HTML, the performance of mobile web has ‘drastically improved’. This was made possible by allowing website owners to build websites with lighter-weight web pages and employing caching techniques of pre-fetching and storing web pages to pre-load it even before the user clicks on it. The result – web pages that earlier took around 3 seconds to load will now take milliseconds to show.

Accelerated Mobile Pages could load much faster when users search for news on Google by getting rid of JavaScript and simplifying the HTML code architecturally.

Many big tech companies and online portals are already on board, including Pinterest, LinkedIn, Twitter, The Washington Post, The Guardian, The New York Times, and The Wall Street Journal.

So, is the Mobile UX Pushed to its Limits?

Thanks to internet conditioning, we want everything quickly. When visiting a news or media website, pages might take time to load. While the text might show, building up might take longer because of ads and images. This painful experience of slow page load times is now being taken care of by Facebook’s Instant Articles and Google’s Accelerated Mobile Pages. These aim at driving more direct traffic for publishers by ensuring the user experience is improved and a foundation is built for creators to deliver their content.

With the speed of the mobile web, it seems a win-win scenario for all parties – first and foremost, the user; the publisher; and the platforms supporting the content. In the long run, the strategy of picking content from a few media houses and serving these quickly to the users can lead to content agenda-setting. Only time will tell how this media distribution and consumption model on the mobile web evolves.

What makes a great UX Design?

UX designing is a dynamic and complex process- to offer the most exclusive experience to your customers, attend to what they have to say. It would be best if you focus your priority on delivering user-centric designs.

What would interest your user base? Nobody other than your customers would be able to answer that better. A user-centric design will help designers cater to the needs of their users through the app.

To start with, research and reference similar mobile apps; this doesn’t mean copying other mobile apps, as what works best for one interface doesn’t necessarily go along with others.

Instead, learn and analyze from your competition- why specific trends work and others don’t. Combine your research with what aligns best for your brand and personalize the user experience, making your UX stronger in the long run. The most general way of validating your product is by testing it with your target audience. Generate a minimum viable product (MVP) at the beginning to settle if your idea’s well-accepted by its core users. If you question how much it will require to produce a mobile app, you can check some online calculators that can aid you in getting some rough estimation.

How can you improve UX for your mobile app?

Every app and its purpose are different; thus, the advancements you want to provide your customers can vary. However, the basic essentialities of delivering a seamless, fast and personalized experience remain the same.

Personalized UX 

Personalization provides unique UX for every app or website on the web. When you align user experience and preferences, users are more likely to stay connected with your app. Personalization becomes more critical when designing eCommerce UX pages. Displaying pop-up messages with personalized names reminds customers of half-abandoned transactions- this adds a customized touch. However, it’s also important to display only relevant content to avoid any counter-effect.

Proper features and speed 

A Google research found that if the page loading time takes more than 5 seconds, the possibility of bounce increases by 90%! Optimizing images and reducing plugins are some of the ways to speed up the mobile loading page. Thus they should remain in your focus to avoid any speed delay issue. The app’s functionality must help users finish their tasks as it’s the first motivation towards downloading any app. Prioritize core features vital for achieving the tasks and offer only those relevant features which encourage even more users to stay connected with your app.

Gesturization Tune 

Gesturization includes users’ actions while navigating and interacting with your app, like tapping, scrolling, and swiping through the screen. Knowing your users’ behavior is crucial for optimizing the gestures according to them. Gestures allow users to engage with the technology through the sense of touch; these famous gestures are tap, double-tap, drag, swipe, and press. Designers should keep these touch- gestures out of hard-reach areas for easy navigation and provide enough tapping space. 

Summing Up

The big fish are emergently experimenting with their UX designs to hold their customer base and provide a seamless experience. Its time designers and leaders focus immediately on what they can learn and experiment on their own for their consumers. The above tips highlight the recommendations for visually pleasing design and reliability. UX design itself should be subtle, simple, and decluttered – the users must feel the pronounced navigation flow.

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What skills would be needed for Telecoms professionals in the 5G world?

Background

We all have a mobile phone and, in this sense, we are all consumers of mobile technology. Over the past four decades, mobile networks have evolved in parallel to the internet. However, the next evolution of the mobile network (5G) has much to offer to the enterprise. Yes, consumers will benefit because 5G is more than 100 times faster than the prevailing network technology (4G / LTE). However, 5G also represents a fundamental change in architecture of the mobile network.

Over the last decade, telecoms has been evolving ‘as a service’. For example, the early efforts of the GSMA to create One API based on IMS (IP Multimedia Subsystem) allows access to payment, location, SMS etc, However, these were niche and experimental.

Now with 5G technologies, the capabilities of mobile networks and internet are converging to a much greater extent. This technical convergence has the potential to create massive opportunities for both enterprises and telecoms. COVID will only accelerate the need to deploy innovative solutions via 5G

Implications for new services

What does this mean for new services?

New services would be network aware. Currently, the service does not depend on the capabilities of the network. But 5G has some unique capabilities that could be leveraged by applications

Specifically, it would be possible to create low latency, high-bandwidth applications running on edge devices with high sensor density. You could also deploy live video analytics, which depends on low latency.

Typically, you would deploy new 5G based services as a partnership between an enterprise, a network operator, and a systems integrator. Hence, depending on the partnership between the telecom operator and the cloud provider, new services can be developed using existing cloud APIs Cloud APIs (ex: AWS, Azure, GCP). Mobile Edge Computing would be a key touchpoint for deployment of new services, especially Edge / IoT services.

Some applications like self-driving cars are still away. But many enterprise applications could be re-imagined through a combination of 5G, MEC, AI, IoT, robotics, AR, autonomous logistics systems etc

Implications for skills

But the biggest impact will be for skills in Telecoms. The integration with the enterprise world creates the need for a different type of application. Traditional telecoms services are B2C and depend on revenue measures like ARPU. With complex applications spanning the telecoms and the Cloud, a new skill set is required for telecoms which could include

• AI
• Edge / IoT
• Cloud
• Cyber Security
• Media
• Augmented reality and virtual reality
• Creating autonomous systems
• End to End services
• Digital transformation
• Robotics
• Project management / Product management – execution

Most importantly, we need the imagination to rethink and retransform services with new capabilities enabled by 5G.

Image source GSMA

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When to Adjust Alpha During Multiple Testing

In this new paper (Rubin, 2021), I consider when researchers should adjust their alpha level (significance threshold) during multiple testing and multiple comparisons. I consider three types of multiple testing (disjunction, conjunction, and individual), and I argue that an alpha adjustment is only required for one of these three types.

I argue that an alpha adjustment is not necessary when researchers undertake a single test of an individual null hypothesis, even when many such tests are conducted within the same study.

For example, in the jelly beans study below, it’s perfectly acceptable to claim that there’s “a link between green jelly beans and acne” using an unadjusted alpha level of .05 given that this claim is based on a single test of the hypothesis that green jelly beans cause acne rather than multiple tests of this hypothesis.

For a list of quotes from others that are consistent with my position on individual testing, please see Appendix B here.

To be clear, I’m not saying that an alpha adjustment is never necessary. It is necessary when at least one significant result would be sufficient to support a joint hypothesis that’s composed of several constituent hypotheses that each undergo testing (i.e., disjunction testing). For example, an alpha adjustment would be necessary to conclude that “jelly beans of one or more colours cause acne” because, in this case, a single significant result for at least one of the 20 colours of jelly beans would be sufficient to support this claim, and so a familywise error rate is relevant.

I also argue against the automatic (mindless) use of what I call studywise error rates — the familywise error rate that is associated with all of the hypotheses that are tested in a study. I argue that researchers should only be interested in studywise error rates if they are interested in testing the associated joint studywise hypotheses, and researchers are not usually interested in testing studywise hypotheses because they rarely have any theoretical relevance. As I explain in my paper, “in many cases, the joint studywise hypothesis has no relevance to researchers’ specific research questions, because its constituent hypotheses refer to comparisons and variables that have no theoretical or practical basis for joint consideration.”

Sometimes it doesn’t make sense to combine different hypotheses as part of the same family!

For example, imagine that a researcher conducts a study in which they test gender, age, and nationality differences in alcohol use. Do they need to adjust their alpha level to account for their multiple testing? I argue “no” unless they want to test a studywise hypothesis that, for example: “Either (a) men drink more than women, (b) young people drink more than older people, or (c) the English drink more than Italians.” If the researcher does not want to test this potentially atheoretical joint hypothesis, then they should not be interested in controlling the associated familywise error rate, and instead they should consider each individual hypothesis separately. As I explain in my paper, “researchers should not be concerned about erroneous answers to questions that they are not asking.”

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The Evolving Role of a CDO in a Data-Driven Market

As a result of the recent pandemic, there has been a surge in demand for data to help safeguard businesses from future uncertainties. With an increasing number of organizations fuelling solutions and driving innovation with data, there is a growing concern with the way that data is accessed, used, and protected. In fact, there was a 126% increase in total fines from 2019 to 2020 issued as a result of the GDPR. These fines will only continue to increase as privacy regulations evolve and expand. 

However, data governance isn’t just about avoiding fines. It also enables organizations to achieve better data analytics, more informed decisions, and improved operational efficiency. As more organizations begin to realize the value of a sound data governance strategy, the role of Chief Data Officer (CDO) has grown in importance and demand.

We recently had the opportunity to chat with Rupinder Dhillon, CDO at Hudson’s Bay Company. Rupinder has worked in data for over twenty years with expertise in data management, business intelligence, advanced analytics, and AI and machine learning. She has worked across industries spanning financial services, software, telecommunications, and now retail.

We had the opportunity to get her perspective on the state of data in 2021.

Q: Welcome, Rupinder. Thank you for sitting down with us. To get started, can you please explain your main responsibilities as a CDO?

I believe that the CDO role is nuanced to the company that you’re in. The role will differ slightly from organization to organization depending on their data maturity, where they are in their data journey, and what data goals they have set. Traditionally, data is thought of as the exhaust that comes from a system. The role of a CDO includes trying to change this mindset from data as just a by-product to a tool that can be used for exploration and innovation. Data is no longer just about reporting.

My role sits at the crossroads of three key areas:

1. providing good governance around data;
2. creating a data-driven culture and driving innovation through the use of data; and
3. making sure the organization has good visibility to performance and key metrics.

Q: How would you define a data-driven culture?

A data driven organization is one that has data and analytics embedded in the culture and the way people work every day, regardless of their function. In a data-driven culture, data and analytics is not seen as a function that is owned by one team but the entire company.

Q: How have you implemented and adopted a data-driven culture?

I’ve adopted the idea of a data-driven culture by positioning data and technology as an ecosystem. Investing in technology that allows data to be more accessible to people across the organization has been a driver for adoption. I call this an ecosystem because the platform is a place in which teams not only pull data out, but also feed data into the platform.

This is important so that all the great analytics that teams are discovering can be available across an organization. Everyone in a company should have the responsibility to not only export data but feed data into the tech ecosystem. For example, if the Logistics teams are working on an analysis in their space, it’s important that the same data and analytics should be available and shared to the Marketing team as well. 

Q: What are the most pressing challenges facing companies who are implementing data governance strategies?

The challenges that companies face depend greatly on the industry they are operating in. For example, data governance for an insurance or financial company is going to be different than that of the retail world. It must be performed with the same level of scrutiny, but the focus of the strategy will differ. Canadian companies need to be thinking about all of the changes that will be coming in the near future regarding data privacy.

Regulatory changes present the opportunity for organizations to think through the questions of:

• What do we want the data relationship to be with our customers?
• How can we design our data strategy from the get-go to not just protect customer data, but also build trust with our consumers?
• What is the legislation and what does it mean to us?

Q: What do you think are the most important variables to consider when developing a data governance strategy?

Across the board, if you’re a company that is working business to consumer (B2C), there needs to be a focus on how to protect customer data and their privacy. Especially when working with ML and AI to power processes, you need to decide where customer data fits and where it should be anonymized.

The most important lesson I’ve learned about data governance thus far is understanding what governance you need, and when you need it. Think about these questions when developing your data governance strategy:

• What are the non-negotiables with data governance that you need to build into your strategy on day one?
• What are the things you’d like to build into your data governance strategy over time?

Building out a data governance program is a process. It’s important to start in small incremental steps, not attempt to boil the ocean. Many data initiatives try to get the attention of corporate leaders; however, they must show business value and impact first. Robust data programs could take 12-24 months or more to realize business value. Most organizations simply cannot wait for this amount of time which results in the program losing traction and fizzling out.

Q: You mentioned the importance of data sharing within an organization. What are the opportunities that you see in implementing data sharing?

There are two parts to data sharing I’d like to address: internal and external data sharing.

I don’t believe that certain teams own certain data. For example, marketing owns customer data, logistics owns supply chain data, and customer service owns call centre data. What I mean by ownership is that those are the only teams who need that information and can use it. Our customer data is equally as important to our merchandise planning folks as it is to those that are running our digital strategy, just as our call centre information is very important to our marketing team.

Organizations still require domain responsibilities, meaning teams carry the responsibility for the data that gets generated through their domain. However, I really buy into data mesh architecture where different parts of the organization are creating data as a product for the rest of the organization to consume and collaborate with.

“Real insights come from the cross-sections of traditional data domains. You miss out on hidden problems and opportunities if you neglect insights that can be found by analyzing trends across departments.” 

When considering the value of external data sharing, I do see opportunities in sharing anonymized information across different companies and different types of organizations (e.g. COVID-19 or demographics data). The COVID-19 pandemic has highlighted the importance of data sharing to better adapt to external environment changes. I have seen some great technologies on the market that make creating an ecosystem of internal and external data sharing more seamless, and most importantly, without needing to share any customer information.  There is a tendency to focus on customer data when considering external data sharing strategies, but there is a lot of value to be gained by exploring the cross-sections of operational or functional types of data to better understand a company’s operations and find opportunities.

Q: What kinds of trends do you expect in the data industry and with data use in the enterprise?

I think there is still a long way to go in Canada around adoption of data and analytics. We’ve made strides forward but there is still a long way to go in order to make data a generally available and usable tool in an organization’s tool box. It is very important to not just democratize data, but democratize analytics capabilities by making them a part of your everyday functions rather than the function of just one team.

With more companies embracing ML and AI, I see a trend towards these technologies being a general purpose tool available across all facets of an organization. I hope to see more Canadian companies embed data and technology wherever possible. This includes driving AI and ML solutions that are actually part of their systems and processes. Not just one-off use cases, but ones that drive and change how an organization functions in their day-to-day.

I’ve seen a lot of digital acceleration and I think we’ll see even more over the next two to five years. The companies that are born in the digital age, like Shopify, are setting the bar for all industries. The organizations who were not born in the digital age, but are prioritizing digital transformation efforts, will be well-served to keep up with competitors going forward.

We’d like to thank Rupinder for taking the time to speak with us and share her insights on the state of data in 2021.

To echo her insight, data-driven organizations are those that have data and analytics embedded in their business processes, departments, and people. The organizations who reap the benefits of both internal and external data-sharing will be positioned to thrive in the upcoming years.

Want to learn more about aligning your business and data strategy? Request a consultation with one of our data experts or browse the largest catalog of solution ready data to determine how ThinkData’s tech can advance your projects. 

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AI in SEO: 5 Ways to Use AI to Boost Website Rankings

Artificial intelligence (AI) has taken the business world by storm with its innovative applications in almost every field. In fact, 50% of businesses use AI in some form or another.

SEO is no exception to this and the use of AI in SEO is growing multifold. From content optimization to link building, AI is being used for SEO in multiple ways.

But, why is it important to use AI in SEO and how does that improve website performance?

Find out the answer in the following sections.

Why is It Important to Use AI for SEO?

Artificial intelligence plays an important role in SEO from keyword research to content optimization. Even the search engines use AI for their ranking algorithms and Google’s RankBrain and BERT are good examples of this.

Using AI in SEO will make the ranking process more efficient.

AI and SEO together form a powerful combination that can help improve website rankings and overall search performance.

Most tasks that you need to do for SEO, like keyword research and optimized content creation, can be done better using AI.

Want to learn how AI can help with SEO?

Find that out in the next section.

5 Ways to Use AI for Search Engine Optimization

Now that you know that AI is important for SEO, let’s understand how exactly it helps.

In this section, you will learn how to use AI for SEO to improve your website’s search rankings and performance. Use these as a guide for using AI for SEO.

Ready to get started?

Here you go.

1. Find the Right Keywords

One of the most common uses of AI in SEO is to find the right keywords to target through your website content.

There are many AI-based keyword research tools that you can use to find relevant keywords in your niche. These tools can also help you select the right ones using metrics like search volume, keyword difficulty, etc.

You can also explore your competitors’ keywords and find keyword opportunities that they might not be utilizing. 

Doing all of these manually would have been an almost impossible and time-consuming feat, but the use of AI makes it extremely easy.

Also, don’t forget to use the right target keywords for your landing pages. Just using the right platforms for building landing pages isn’t enough. You also need to optimize your page content for the right keywords.

For example, a call center that provides different types of customer services can create a variety of landing pages by targeting multiple keywords. By leveraging AI-powered tools, they can find the right keywords to target for each of these pages too. This way they would be able to rank for each keyword that they target. 

2. Create and Optimize Content

AI is not only good for finding keywords but also for creating content that is optimized for SEO. 

You can use these insights to create better content on trending topics and outrank your competitors. Furthermore, visual content has the power to attract an audience Different types of infographics, graphs and charts, screenshots, photos can rapidly improve user engagement.

You can use these insights to create better content on trending topics and outrank your competitors.

You can further optimize your content by using AI-powered online editing and keyword optimization tools like Grammarly and Surfer.

3. Discover Link-Building Opportunities

Another important aspect where AI helps with SEO and website optimization is finding link-building opportunities. 

There are numerous AI-powered SEO tools that you can use for this purpose.

Semrush, for example, provides a brilliant Backlink Audit tool that provides you with opportunities for link building. It will show you a list of sites where you have published content but have not asked for a backlink.

You can use this list for your link-building outreach campaigns and get quick backlinks without much effort. This is one of the easiest tactics to build links and a brilliant example of how using AI in SEO makes life easier.

4. Optimize Your Site for Voice Searches

One of the lesser-known uses of AI in SEO is to optimize your website content for voice searches.

How does AI help with that?

AI-based tools like AnswerThePublic can tell you what questions people ask around a keyword. So, instead of targeting normal keywords, you can target question-type long keywords, which is what people use for voice searches.

Here’s an example of this tool at work:

Image Source

5. Conduct SEO Audits

One of the most important, yet complex, tasks for SEO is to conduct regular site audits.

Site audits are important to find and fix issues that may affect your website’s search performance. Such issues could be anything from broken links to duplicate content and should be fixed promptly.

This is where AI can help you.

AI-based SEO tools like Semrush and other alternative tools can conduct extensive site audits within minutes, if not seconds. These tools can provide you with a ready-to-use list of SEO issues that need to be addressed and also sort them out by priority.

Image Source

Conclusion

AI is the future of SEO and if you want a competitive edge, you need to embrace it early on. 

The uses of AI in SEO are numerous and can make the entire process quicker and more efficient for you. Start using AI-powered SEO tools to leverage this technology and improve your website’s rankings and overall performance.

Ready to embrace AI to grow your business?

All the best!

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How does Machine Learning and Retail Demand Forecasting promote business growth

Machine learning in retail demand forecasting has transformed the retail industry. The primary aim of using machine learning in demand forecasting is to predict the future demand in products and services accurately so that the product can be designed and re-designed accordingly. It is one of the unique Retail IT Solutions that reduce product wastage while mitigating inventory issues. Furthermore, it accelerates the data, eliminates the stock-out situations, provides accurate forecasts, and speeds up the process of unstructured data analyses.

What are the principles for Machine learning demand forecasting?

ML technology principles are based on Machine learning forecasting. The ML-backed software first understands the business’s demand patterns and then conducts predictions of the future targets using its self-learning capabilities. The forecast generated by the software benefits both users and the company.

Now let’s learn how retail demand forecasting uses machine learning to promote customer satisfaction and businesses growth.

Automation in demand forecasting

Implementation of machine learning solutions in demand forecasting automates the entire process. Forecasting demand is a time consuming and tedious process and involves several people. There are high chances of human error in the traditional method of demand forecasting. Even the slightest of the mistakes may result in a huge loss of both brand value and money for the retail business owners.

The combination of demand forecasting and machine learning allows retailers to save their resources and time. Plus, machine learning in retail gives accurate results while eliminating the need for human assistance or specialists.

Optimal accuracy in planning inventory

Accuracy is one of the crucial factors of forecasting in retail demand planning. Businesses need to use a validated forecasting method that comes with a guarantee of accuracy. The prediction accuracy is essential because it helps executives determine the demand value of their products and services in the market. And the slightest inaccuracy can disrupt the entire process.

Advanced machine learning algorithms and demand forecasting together automate the tasks involved in inventory management. It relieves the retailers from the hassle of managing stock. The inventories are sorted according to the user interests, requirements and profit value. It is one of the primary reasons for implementing ML solutions in demand forecasting.

Enhanced business and customer relationship

It is another significant benefit of using machine learning in retail. As mentioned earlier, machine learning accurately forecasts and predicts the needs of users well in advance. The scenario eliminates conditions such as old products selling or stock-out. Predictions and forecasting can be used to know about the future preferences of customers. It contributes to an enhanced relationship between the brand and customers as the business will provide products/services in trend.

Increase in profitability of the business

Using machines in demand forecasting helps retail businesses in increasing their profitability. The customized ML solutions automate demand forecasting and reduce operational costs by predicting an ideal time for product sales. Eventually, the companies can ensure optimized and better business operations. In addition, it helps them save a huge amount that otherwise goes into hiring talent and compensation of the losses.

Effective sales and advertising campaigns

Demand forecasting and machine learning together can handle sales and marketing campaigns efficiently. The advanced ML software helps in analyzing the marketing trends. Plus, it also predicts the future demand and market conditions. As a result, retail businesses can determine what improvements in their product and services need to stay in demand among the customers.

Using this crucial info, the businesses can save cost and time and increase sales and potential leads, which is otherwise difficult with the traditional method. Thus, the implementation of machine learning in retail forecasting provides countless benefits besides access to future demands.

Machine Learning makes it easier to adapt to change

ML demand forecasting can sense the forecast and update the programme according to the available data. In other words, the forecast can be updated on a weekly or daily basis according to the change in the data of stock, warehouse, etc. The program uses recent actual data to regenerate and run a new forecast. The metrics and accuracy of the latest estimates can also be calculated using the base forecast. Comparisons can be made with the updated and old results, allowing the executives to review performance, demand and factors.

Concluding Word

Machine learning isn’t limited to sales and demand forecasting. It has eased forecasting future trends, customer engagement, marketing campaigns, brand development, financial risks, resource usage, etc. The potential of this futuristic technological tool depends on how well the retail business owners take advantage. As of the current scenario, machine learning in demand forecasting adds value to businesses and helping them to meet customer demands. In a nutshell, there are endless possibilities for this technology. It also opens opportunities for retail business owners.

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Best AI Certifications in 2021!

Those seeking a career in artificial intelligence (AI) can have an easy task when you have an AI certification from a reputed institute.

Artificial Intelligence (AI) is a skill that is programmed and designed so that the machines behave and think like a human. It is an imperative part of our daily lives and is used in many daily service areas. The introduction of artificial intelligence has brought the idea of ​​an error-free world and is gradually getting introduced in all fields to increase automation and provide accurate and faster results.

Becoming a certified AI professional is the key to a satisfactory career in the AI ​​domain and can be a tool to elevate your career for better projects and roles.

However, getting certified can be a huge task, as numerous institutes are offering AI certifications. So how do you choose the best amid the lot? Well! We have made that chore easy for you as we share the six best AI certifications in 2021, which will help you scale up your career.

• MIT AI Online Program: Massachusetts Institute of Technology(MIT) offers various online certification programs for artificial intelligence. The certification program by MIT offers practical knowledge in AI that will help you transform your organization into an efficient, sustainable, and innovative organization of the future.

This program will help you understand the design principles and applications of artificial intelligence in various industries. You will learn the various stages involved in AI-based product design and the basics of the machine and deep learning algorithms and apply these insights to solving practical problems. Your goal is to create an AI-based product proposal that can be submitted to your internal stakeholders or investors.

The program also offers the double perspective of management and AI, thus giving you a sound knowledge of AI-based technologies through the eyes of the business.

URL: https://bit.ly/3kPmXqH

• Machine Learning Engineering for Production (MLOps) Specialization by Coursera: Learn by the leaders. Created by none other than Andrew Ng, Machine Learning Program by Coursera has been cited as one of the best in the AI certification programs. Effective deployment of machine learning models requires more common capabilities in technical fields such as software engineering and DevOps. Machine learning engineering for production combines the basic concepts of machine learning with the functional expertise of modern software development and engineering roles.

Through this program, learners are introduced to basic machine learning ideas, including recognizing statistical patterns and data mining.

URL: https://bit.ly/3xZ9gt7

• AI Certifications by the United States Artificial Intelligence Institute (USAII™): The institute offers three major cross-platform (vendor-neutral) certification programs for aspiring AI professionals – for an engineer (students or working professionals with limited industry experience), consultant (for students having Master’s degree with limited industry experience and working professionals with more than two years of industry experience), and scientist (for working professionals with more than five years of industry experience) role in the field of AI. The need to evolve from being data-driven workflows to AI-driven has opened numerous job opportunities in the industry. Thus the three AI certifications, Certified AI Engineer (CAIE™), Certified AI Consultant (CAIC™), and Certified AI Scientist (CAIS™), provide the industry-relevant skills in AI.

All three programs are self-paced and offer preparatory study kits to the candidates comprising study books, videos, workshops, and practice code. Getting a vendor-neutral AI certification will have a multi-fold advantage. USAII™ claims to close the AI talent gap with these certifications across the globe.

URL: https://www.usaii.org

• IBM Applied AI Professional Certificate (Coursera): Designed by the global leader in Tech IBM, the Professional Certificate in Artificial Intelligence caters to professionals who want to work as AI developers. This plan will give you an in-depth understanding of AI technology, its applications, and use cases. You will be familiar with concepts and tools such as machine learning, data science, natural language processing, image classification, image processing, IBM Watson AI services, OpenCV, and APIs.

You will learn practical Python skills to design, build, and deploy AI applications on the web with this professional certificate without a programming background. These courses will also enable you to apply pre-built AI intelligence to your products and solutions.

URL: https://bit.ly/36RG4s7

• Coursera Artificial Intelligence Courses: Coursera provides a huge variety of certification programs and specializations in the field of AI. These programs have been designed in association with the world’s top universities and data science schools, including leaders in the AI industry. The programs created by Coursera introduce the students to the latest tools, concepts like Artificial Neural Networks, Deep Learning, TensorFlow, Python programming, and reinforcement learning.

The program ranges from beginner-level courses to senior-level programs for experienced AI professionals.

URL: https://bit.ly/2V2pHGn

• Enterprise AI and Machine Learning by Cloudera: The enterprise AI and Machine Learning program by Cloudera enables the learners to shift their focus from technology to the result, i.e., the outcomes, and thus empower the ongoing optimization in the field. Only through the industrialization of AI will you be able to shift focus from technology to outcomes, empower continuous optimization and learning across the enterprise, and turn data into predictions at any scale, anywhere.

With this program, an applicant can build and deploy and scale AI applications with a repeated industrialized methodology that can turn available data into insightful decisions.

URL: https://www.cloudera.com/about/machine-learning.html

These are some of the best AI certifications in the market. You can visit their website individually to gain more knowledge about each of the certifications and get started on your AI career path. You should carefully select the program that helps you best to learn essential AI skills and give you a forever valid digital badge (to brand you on social media like LinkedIn, Facebook, Instagram, and others). This will undoubtedly help you to find a job or promotion or increase your personal brand value as an AI expert.

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No Code AI, No Kidding Aye!

When was the last time you did something meaningful for the first time? For me, that was in the last week of June’21. Just two weeks into my stint at Subex, I made an ML prediction model, my first one! Yes, I know that is nothing earth-shattering, but before you start rolling your eyes at my juvenile glee and start judging, let me tell you that I do not know how to code. I cannot code to save my life and the last time I wrote something that had a semblance of a code was two decades back when I was in graduate college.  So, yes, I am elated; not in the least because I made a very simple ML model, but because of the feeling of freedom and empowerment that I felt when I saw each one of my process steps in the pipeline lighting up in green and running its full course and culminating in successful output. In my mind, it is the kind of emotion one goes through when you have been handicapped for a lifetime, and then, one fine day, you get a bionic limb that liberates you and lets you walk freely once again.

The future potential of No Code/Low Code platforms

Gartner’s research points to the emerging trend that digital transformation initiatives have triggered an insatiable demand for custom software development. This, in turn, has ignited the emergence of citizen developers and citizen data scientists who are outside the traditional definitions of an IT developer or an ML developer. This paradigm shift has influenced the rise of no-code and low-code platforms. According to Gartner, on average, 41% of employees outside of IT – or ‘business technologists’ – customize or build data or technology solutions, and they are sticking their neck out confidently to say that that, by the end of 2025, 50% of all new low-code clients will come from business buyers that are outside the IT organization and 65% of all application development will be low code by 2024.

Enough of numbers for now. I guess we all get the drift – Low code/No code development platforms are the next big thing in software development. So, is this another technological development whose impact will stay limited to large, for-profit corporations and enterprises, or, will this have a greater, more purposeful bearing?

The profound impact of No Code platforms

In 2005, the Indian parliament passed a historic and landmark bill – The Right To Information Act, or RTI, as it is popularly known. This Act took the key that was needed to access data related to most of the day-to-day functioning of government bodies, from the hands of limited law enforcement and judiciary entities and passed it into the hands of the common man. Suddenly, everything changed. The fundamental societal framework that government functionaries could do whatever they wanted and get away with it unless someone with a lot of time, money, patience, courage, and determination could force the hands of the law using judicial process, was turned on its head. Anybody who was a citizen of India could pay a paltry sum and demand specific information from almost any government entity and was entitled to get that information. It was a true watershed moment for the democratic fabric of this great nation. It made governance more responsible and accountable, and it paved the way for numerous improvements and transparency at the grassroots level, and that, in the true sense, was a transformation. In case you are wondering how this is related to No Code AI platforms, hold on just a little bit more.

It was never the case that prior to the RTI Act getting passed, there was no data. Oceans of data existed, but what did not exist was a framework and structure in which that data could be leveraged by every citizen. Until then, data could only be sought and used by limited entities and institutions – legislature, law enforcement, and judiciary and often, they only sought and used data for purposes of utmost legal importance and priority. Compare that to the situation we have at hand in the private and business sector. Humungous amounts of data exist, but how can you leverage the true potential of it if the value extraction power is in the hands of a few limited people who need to have highly advanced, technical skills? Creativity and the power of imagination are not always ensconced inside technical or programming knowledge. There are so many business users who have fantastic ideas which never see the light of the day, either because it is not considered a priority, or simply because there’s just not enough bandwidth of expensive technical resources to spare to chase up every idea that is being tabled. In most cases, the process of innovation in organizations works like the highly competitive entrance examinations to prestigious colleges – it is a process of eliminating as many as possible, rather than retaining everyone who might have potential.

With RTI, anybody who wanted to check a hypothesis could ask for relevant data and had the right to receive that data within a stipulated timeframe. Suddenly, corruption became one step more arduous as the usage of data became truly democratized. NGOs and committed citizens who wanted to make real fundamental changes started gathering and foraging through data to identify patterns and anomalies and started asking questions, most of which were uncomfortable ones to answer but had the power to alter the basic fabric of the system.

AI has the power to change the way we eat, sleep, breathe and live. In business, it has the potential to transform fortunes and deliver exceptional customer experiences at scale, but the question we need to ask is – Given a choice, do we want to restrict and throttle this power, due to natural limitations, within the hands of a few people who need to have highly technical and specialized skills to know how to fully leverage it? Imagine the possibilities of this power in the hands of millions of creative and logical people who can dream up out-of-the-world applications for the data assets that we sit on without everybody having to be a software programmer or a technical data scientist. That would be true democratization of data and AI, and No-Code platforms are paving the way for this revolution.

Note: This blog post is part one of a two-part series on the No-Code AI platform revolution. The next part will cover the challenges of the AI model building that No-Code AI platforms address.

Till then, stay tuned…

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DSC Weekly Digest 3 August 2021

There were several interesting announcements this week about the Rise of the Metaverse. Depending upon who you talk to, it’s the next big thing, Internet 3.0, Snow Crash careening into Ready Steady Go, with vibes of Tron thrown in for good measure. It’s Virtual Reality 2.0. And it’s coming to a screen near you tomorrow … or maybe in fifty years. You can never tell with these kinds of things.

There is a certain innate similarity between virtual reality and self-driving cars. To hear the press releases from either 1999 or 2015, VR and autonomous vehicles were literally just around the corner, an engineering problem, not a conceptual problem. By 2021, the first truly consumer autonomous vehicles were supposed to be coming off the assembly lines, and VR should have been achieved by now. Instead, AVs are still at least a decade away and truly immersive, fully interactive VR should be a thing.

Now, anyone who games regularly can tell you that immersive realities are definitely here – so long as you’re very careful to constrain how far out of the box someone can go. Anyone who’s played Halo or Overwatch or even Dead Red Redemption can tell you that the games are becoming quite realistic, and arguably games such as the Sims (version x) attest to the ability to have multiple individuals within a given simulation.

As with AVs, the challenge ultimately isn’t engineering – it’s social. Second Life explored the themes of virtual reality in a social sense. What happened afterward was simple: people discovered that virtual conversations and virtual sex with virtual avatars was, at the end of the day, boring and more than a little creepy. It was like going to a bar without any alcohol. 

We enjoy games precisely because we are, to quote Terry Pratchett, narrative creatures. We are natural storytellers, and we love both being told and participating in stories. We love pitting ourselves against others, seeing ourselves as fighting the good fight or solving deep mysteries that would have stymied Sherlock Holmes. Psychologists also talk about the dangers of escapism, but games are attractive primarily because most IRL stories are not very exciting.

There’s some significant money to be made in Extended Reality (XR), but it’s important to understand there is that for it to truly work, XR needs to concentrate as much on the metadata, the story, as it does on the various communication protocols and representations.

The latter is not insignificant, mind you. The virtual world is the quantum cognate of the real world. Identity and uniqueness are intrinsic to the physical world, and creating duplicates that travel through real space and time is a nearly insurmountable problem. In the virtual world, however, uniqueness and identity are simply abstract concepts, and creating copies that can persist for any significant length of time can prove difficult at best (this is what blockchain is supposed to do, but we’re discovering the very real energy costs in even approximating uniqueness).

Yet, ultimately, the real challenge will come when the various players in this space recognize that without compelling content where immersion means that people become a part of the narrative, not simply an avatar walking around stiffly in a pretty landscape, XR will fail. I’d also like to believe that ultimately it will take agreement on standards for all of the fiddling bits, like identity management, concurrency, data flows,, and so forth, to all come together so that moving from one narrative to another becomes feasible (or even makes sense), but I suspect that will only come once the landscape has become nearly irrevocably fractured. There are too many people with dollar signs in their eyes at this stage to expect any difference.

What does this mean to data science? Easy – a game is simply a simulation with a better plot. AI is intimately tied to the concept of Metaverse, and will only become more so over time.

Some Recent Changes

There are a couple of new changes to the newsletter. The first is changing the DSC article listings so that they show authors and publication dates. We’re proud of our writers, and I feel that by posting who wrote what will make it easier for you as a writer to go to those writers that you enjoy most, as well as helping you discover new and different viewpoints. Clicking on the writer links will give you a feed showing all of their previous articles.

Another, more subtle change is that as a member of DSC clicking on a Tech Target article will take you to the article without triggering the paywall. You can now enjoy more of our parent company’s content, and get perspective from industry leaders. It will also help us track what’s most important to you, our readers. Note that you can only see TechTarget content when coming from the Newsletter or from the DSC site itself.

In media res,

Kurt Cagle
Community Editor,
Data Science Central

To subscribe to the DSC Newsletter, go to Data Science Central and become a member today. It’s free! 

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As the Digital Workforce Changes, So Will the Economy

Image credit: Unsplash. 

This is an Op-Ed about the relationship between remote work, the economy and our physical communities health and well-being. 

As people are glorifying the remote work WFM trend, for some reason the dark side of digital transformation isn’t being told. Yet technology, data science and the future of artificial intelligence impacts how society functions, how economies evolve and now even how neighborhoods and communities will ‘feel’. 

There is a very stark reality that’s not being talked about during the pandemic very much. It’s actually how remote work (the WFM movement) is bad for the economy. While it gives workers more freedom and saves on time and productivity, the powerful side effect is less spending for local businesses and small businesses that depend upon commuters.

While people like to blame the pandemic for this, it’s actually digital transformation leaders that permanently upend the small and medium-sized business (SMB) sector here as accelerating the WFM remote work trend. While companies like Zoom, Microsoft and others thrived during the transition, many SMBs and local business are already gone including restaurants, coffee shops, independent retailers, dry cleaners, etc… you get the idea.

Basically all the things commuters do on their daily runs to and from the office could be somewhat upended. So is the real story that remote work is bad for the economy or that digital transformation has a dark side on the nature of the economy?

The Impact of Remote Work on the Economy is Underreported 

In 2020, the number of people working from home nearly doubled, to 42% of America’s workforce, according to the Bureau of Labor Statistics. Several companies in the tech sector are giving employees the option to consider hybrid or full on forever remote work. Less commuting also means lower consumption of oil, among many other things. So the impact of remote work on the economy will in some ways have better productivity for Big Tech giants but severely impact the sustainability of small businesses who already have tight margins.

CNN goes on to state that you pay train conductors’ salaries with your subway fare. The dry cleaner by the office and the coffee shop around the corner all count on workers who have been largely absent for nearly a year and a half. The pivot to hybrid and remote work is not very well understood, since it’s so recent and new. But the dark sides of digital transformation are certainly being suppressed from the mainstream news.

Those train tickets or lattes really do add up and taking them away can also hurt local transportation networks and the future of urban commuting itself. The WFM movement could accelerate the profits of certain companies while disrupting others. For many SMBs this is really sad since the pandemic marks a permanent shift of how their business model will no longer be viable, this impacts local economies, the destruction of neighborhood commerce and SMBs.

Is the Digital Workforce Harmful to Local Economies?

Companies like Microsoft, Zoom, or Peloton will argue the pandemic is to blame; they were just opportunists who helped us through the transition. But digital transformation will, in this way, accelerate wealth inequality and low paid service jobs will be impacted by those lost small businesses. The huge corporations profiting during the lockdowns and pandemic work situations hugely accelerated their revenue and seek to keep the switch of all things digital and remote work as a permanent trend.

In the debate of whether remote work harms or helps the economy, a lot is lost in translation.

To put this in perspective, New York’s public transport system is the largest in the nation and at the heart of the city’s economic power. Before Covid, it brought in nearly $17 billion in revenue. But with ridership still depressed, revenue predictions have been slashed too. The Metropolitan Transit Authority received nearly $4 billion in government funding through the CARES Act, but fare and toll revenues aren’t expected to come back to their previous levels until 2023, according to a report from the Office of the New York State Comptroller earlier this year (CNN).

The rapid spread of the Delta variant and the coronavirus becoming endemic basically means the digital transformation natives are validated, they will further disrupt the local SMB sector all over the U.S. and to some extent, most urban centers in the world. This is a huge transfer of wealth and for local economies will have real and significant costs that aren’t being monitored or regulated.

Corporate Individualism and the Push for the Digital Workforce Could Upset Communities and Small Businesses 

What’s for the individual worker (actually the Big Tech corporation) is not always good for the collective. It’s great to have the freedom as a knowledge worker to work remotely, we don’t want to hurt local businesses or impact the economy with our decisions. But just remember that when digital transformation occurs, there’s a dark side of the corresponding disruption of the way things were.

The pandemic has been a grand global experiment in the costs and benefits of a remote workforce. But long before the coronavirus hit, many people worked from outside offices. When opportunistic digital value providers can benefit from the pandemic as we have seen with software services, advertising, social media, gaming or entertainment – what happens in the real world isn’t talked about as much.

The artificial intelligence, cloud computing and software as a service models at the backbone of these transformative new services that cater to the remote worker, and the more remote work wins in the equation of the digital workforce, the harder it becomes for many brick-and-mortar retailers, local shops, services and businesses to even have the opportunity to make a living. In this way the best companies in AI, cloud and subscription software services are disrupting the rest of society and taking their money (wealth distribution, wealth inequality rising and exploiting their advantage against less digitally native companies).

What if Digital Transformation is Not All Good?

We used to fear what Amazon was doing to local retailers, but now it’s Microsoft as well. It’s Zoom, Peloton and so many others who have triumphed when many of us are at our most vulnerable. Hybrid work, remote work and freelancing is likely here to stay to some extent in a post-pandemic world.

More than two-thirds of professionals were working remotely during the peak of the pandemic, according to a new report by work marketplace Upwork, and over the next five years, 20% to 25% of professionals will likely be working remotely. 

By late April, more than half of all workers, accounting for more than two-thirds of all U.S. economic activity, said they were working from home full-time. According to Nicholas Bloom, an economist at Stanford University who has studied remote work, only 26 percent of the U.S. labor force continues to work from their job’s premises.

Some, including several Silicon Valley giants, have announced that they will allow employees to work from home permanently. Yet huge swathes of the labor force are unable to work remotely, and experts say these developments could have profound implications for the economy, inequality, and the future of big cities. Of course Silicon Valley love the idea of making remote work as permanent as possible, they are the main beneficiaries of the new normal!

So what will the new normal look and feel like? The destruction of local communities? “Job ads increasingly offer remote work and surveys indicate that both workers and employers expect work from home to remain much more common than before the pandemic,” Goldman Sachs economists said in a note to clients.

Digital transformation leads have accelerated the WFM reality with their products, services and getting us hooked to new ways of life that involve spending less on local businesses and going to the office much less, if at all. Now employees crave more hybrid work convenience or even a full transition to remote life. The danger to local small businesses and neighborhood commerce is being totally ignored.

Indie Retailers and Local Businesses will be Severely Impacted by Remote Work

In Capitalism, we just assume a natural selection of how businesses compete for advantages is fair or par for the course. But remote work stacks the cards even further against the little guys, the indies, the small Mom and Pops store, the family business, the struggling immigrants, the low paid workers of retail and so forth. Remote work will decimate the local economy and significantly degrade the experience and immersion of neighborhoods and communities, and in 2021 we aren’t even talking about it.

If lockdowns weren’t positive for local businesses who had no customers, what will the WFM be for them honestly speaking on one third to half less foot traffic? Small businesses that used to depend on traffic from offices, won’t just struggle, most of them will gradually go out of business. 

Remote work is mainly the luxury of knowledge workers, but here we are widening the gap of white and blue collar to an absurd degree. It basically guarantees that much of the lower middle class falls lower on the spectrum while some in the upper middle class get a lot of more benefits. Remote work weirdly augments inequality and if I was an economist, I’d be ringing the alarm bells. The WFM movement is great, until you realize not everyone will be along for the ride.

The remote work trend is likely to accelerate wealth inequality and lower the quality of life and well-being in many communities as the demand for many kinds of SMBs dries up due to less foot traffic in the new normal of the digital workforce. 

It’s perhaps the weirdest untold story of the pandemic people ignore unless it impacts them personally. The WFM trend is not all good, and its economic impacts to the SMB sector is going to be significant and likely, permanent. 

Source Prolead brokers usa