Big Data Technology Importance in Human Resource Management

Big data in human resource management refers to the use of several data sources to evaluate and improve practices including recruitment, training and development, performance, compensation, and end-to-end business performance.

It has attracted the attention of human resource professionals who can analyze huge amounts of data to answer important questions regarding employee productivity, the impact of training on business performance, employee attrition, and much more. Using sophisticated HR software that gives robust data analytics professionals can make smarter and more accurate decisions.

In this article, let’s dive further and understand the role of big data technology in the field of HR, in today’s fast-paced world, is done. Where there is the need of analyzing massive quantities of different information.

Recruiting top talent for the firms is the primary task of HR departments. They are required to select candidates’ resumes and interview appropriate applicants till they get the right person they are looking for. Big data offers a broader platform for the recruitment process, which is the Internet.

By integrating recruitment with social networking, HR recruiters can find more information about the candidates, such as personal video pictures, living conditions, social relationships, ability, etc., so that the applicant’s image becomes more vivid and can recruit the right fit. Moreover, the candidates can learn more about the organization for which they will be giving an interview of the information and the recruitment process becomes more open and transparent.

Compensation is the most essential indicator, which attracts potential applicants, and getting a salary is one of the main objectives of employees to participate in the work. Traditional performance management systems often have more qualitative and less quantitative terms and compensation is out of touch with performance results.

Data analytics are solutions that identify meaningful patterns in a set of data. They are helpful to quantify the performance of a firm, product, operation, team, or even employees to support the business decisions. Data compilation to manipulation and statistical analysis are the core elements of big data analysis.

With the help of big data technology in performance management, professionals can record daily workload, the specific content of the work, and the task achievement of each employee. The professionals who have done talent management certification programs will have the advantage to receive better compensation. Sophisticated HR management software, which performs these operations enhances the work efficiency and also reduces enterprise investment in human capital. They are also useful for calculating salaries automatically to get better insights on performance standards.

Employers can gather health-related data on their employees. As a result, more attractive and beneficial packages can be created. The certified HR professionals will get to enjoy more perks too. It is crucial to note that the organizations must be transparent about what they’re doing to avoid legal concerns related to discrimination practices. This can be done by revealing how they are collecting and using this data.

Workforce training is an important part to enable the sustainable development of a business. Successful training can enhance employees’ level of knowledge and improve their performance. So that firms can keep their benefits of human resources in fierce competition and increase their profitability.

Traditional employee training needs a lot of manpower, material, and financial resources. With the advent of big data, information access and sharing have become more convenient. Employees can easily search and find the information they want to learn through the network at any time or anywhere. Workforce big data analysis – makes use of software to apply statistical models to employee-related data to optimize human resource management (HRM). It is also helpful to record data of studied behaviors of each employee, who not only can use the online system to analyze their own training needs but also can choose their favorite form of training.

The role of big data in human resource management has become more prominent. The value of data has certainly accelerated the way a business functions. This rapidly-growing technology enables HR professionals to effectively manage employees so that business goals can be accomplished more quickly and efficiently.

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Open AI Codex Challenge Seen By the Participants

On the 12th of August, Open AI hosted a hackathon for all those interested in trying out Codex. Codex is a new generation of their GPT-3 algorithm that can translate plain English commands into code.

We at Serokell thought it would be interesting to try this out: right now free access to the beta is accessible only to a small group of people. One of our teammates got access to it after being on the waiting list for over a year.

What was the format?  

The point of the challenge was to solve 5 small tasks that were the same for everyone to test the system. To be fair, they were quite simple – maybe because Codex can’t solve complex problems. To give an example, one of the tasks was to use pandas’ functionality to calculate the number of days between two dates in a string. There was a simple task dedicated to algorithms as well: for a binary tree it was needed to restore the original message. 

Our main motivation was to see what Codex can do, how well it understands tasks, and monitor the logic of its decisions. Spoiler alert: not everything was as great and smooth as during the Open AI demo!

What was the problem?

The first problem was connected with server lagging – maybe the company wasn’t ready for such a huge number of participants (a couple of thousands). Because of that, we wasted a lot of time trying to reconnect. Interestingly enough: the leaderboard had a weird logic. The solutions were rated by the time of completion, not by the time needed to solve the problem. So people who were late for the beginning of the challenge were apriori low on the scoreboard. 

To us, it seemed that Codex is not a very smart coder. First of all, it made quite many syntax mistakes. It can easily forget the closing bracket or introduce extra columns. Because of that, the code becomes incorrect. It really takes time and effort to catch these errors!

Secondly, it seems that Codex doesn’t know how to work with data types. You as a programmer have to be very careful, or the model will mess things up. 

For instance, in the previous example of a task that simply is counting days between dates, Codex messed up the sequence of actions for us. It forgot to convert string to date and tried to perform an operation with it as it is. 

Finally, the solutions that Codex proposes are not optimal. It’s a huge part of being a good programmer: to understand the task, break it down into realizable pieces and implement the most optimal solution in terms of execution time and memory. Codex does come up with some solutions but they’re far from being the most optimal ones. For example, when working with the tree, it wrote a while cycle instead of a for cycle and added extra conditions that weren’t in the initial task. Everyone knows that writing while loop instead of for loop is kind of a big no-no. 

Conclusion

All that said, it’s worth saying that Codex can’t be used as a no-code alternative to real programming. It’s unclear who Open AI is targeting with this solution. Non-programmers can’t use it, for the reasons mentioned above. Programmers would prefer to write code from scratch than sit and edit brackets in the Codex code. 

Before it was said that Codex will be behind the Copilot, the initiative was realized together with GitHub. But firstly, it doesn’t work as an autocomplete tool like PyCharm. The majority of the team doesn’t write code in GitHub and uses it simply for project management. So it’s unclear what Open AI is going to do with Codex.

Anyhow, it’s an interesting initiative that has the potential to greatly improve the more people use it. So perhaps in the future, it will become a super user-friendly alternative to no-code solutions for non-programmers.

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Scaling Data Science And AI To Boost Business Growth

Data science and AI has become a requirement for business growth. The technology has advanced enough to predict customer’s choices and satisfy their needs. 

The volume of data generated per day is predicted to reach 463 exabytes by 2025. On the Internet, the world spends about $1 million per minute on goods. This huge amount of data, known as big data, has increased the need for qualified data science workers. 

According to the US Bureau of Labor Statistics, the employment of data scientists is predicted to grow 15% by 2029, much faster than the 4% average for all occupations. Graduates with exceptional data management skills are preferred both in large corporations and small businesses.

Almost 79% of CEOs believe that implementing artificial intelligence in companies will make their jobs more efficient and easier. AI results in 50% more sales, up to 60% cost reductions, and more. 

Leveraging AI helps you to reach out to your target audience more efficiently, nurture their needs and increase revenue. For instance, you can improve your landing page relevancy and improve the ad quality score for increased conversions. 

Sales and Marketing

AI-powered apps may soon be able to manage all of your everyday tasks. AI-powered predictive content tools enable marketers to be more strategic while also reducing workload. Spending less time on less profitable possibilities and more time on your most profitable segments will allow your sales force to enhance its win rate, cover more ground, and eventually increase revenue.

AI-powered marketing applications crawl your website for blog articles, case studies, white papers, ebooks, videos, and other content. For a complete omnichannel approach, insights can be leveraged to engage visitors across email, online, social, and mobile channels. AI can assist with everything from managing your calendar to scheduling meetings to appraising a sales team’s pipeline by automatically executing these things for you or making them significantly easier by making recommendations based on your prior usage data. 

As a result, companies may now engage in one-to-one value marketing, which was previously impossible without significant scalability.  This enables the system to predict what each user wants to buy. AI knows what to market since it closely monitors customer behaviour. Salespeople devote hours to study and interviews in order to discover what AI already knows.

One such tool to ease your work is Finteza. It offers analytical solutions and the advertising engine. Analytics aids in the collection of data about your site, such as user behaviour, traffic quality, weak points, and inefficient advertising channels. The advertising engine allows for the management of online campaigns, the generation of banners, and the placement of targeted adverts all from a single interface. 

Accurate Customer Demand Prediction

Today, predictive analytics is all about linking disparate systems and data sets to conduct adequate analysis and extract meaningful information from seemingly chaotic data. Advanced analytics-powered solutions can significantly cut costs associated with failures, customer attrition, and other factors.

Data collection and analysis on a bigger scale can help you detect emerging trends in your market. Purchase data, celebrities and influencers, and search engine searches can all be used to determine which goods individuals are interested in. Clothing upcycling, for example, is becoming more popular as an environmentally conscientious approach to update one’s wardrobe. According to Nielson research, 81% of consumers strongly believe that businesses should assist in improving the environment.

Demand prediction is an important part of building a thriving business. If you take a look at a firm that is growing, it means that they have a growth strategy in place. In demand prediction, you calculate a firm’s past sales and then their potential growth rate. 

Data processing, mining, analysis, and manipulation assists in the demand forecasting of an organization. You can analyse the historical data of your competitors’ performance, the firm’s ROI or attrition rate and predict the future just with the help of AI and machine learning. 

Acxiom, IBM, Information Builders, Microsoft, SAP, SAS Institute, Tableau Software, and Teradata are among the major predictive analytics software and service companies.

By remaining updated on the behaviours of your target market, you can make business decisions that will put you ahead of the competition.

Final Thoughts

In business management, the trend is toward customer-centricity, personalization, and data-driven decision-making. Regardless of how organisations do this, being open and adaptable to change puts them one step closer to remaining competitive. Start leveraging the power of data science and AI to elevate your business growth. 

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The Growing Importance of Data and AI Literacy – Part 2

This is the second part of a 2-part series on the growing importance of teaching Data and AI literacy to our students.  This will be included in a module I am teaching at Menlo College but wanted to share the blog to help validate the content before presenting to my students.

In part 1 of this 2-part series “The Growing Importance of Data and AI Literacy”, I talked about data literacy, third-party data aggregators, data privacy, and how organizations monetize your personal data.  I started the blog with a discussion of Apple’s plans to introduce new iPhone software that uses artificial intelligence (AI) to detect and report child sexual abuse.  That action by Apple raises several personal privacy questions including:

• How much personal privacy is one willing to give up trying to halt this abhorrent behavior?
• How much do we trust the organization (Apple in this case) in their use of the data to stop child pornography?
• How much do we trust that the results of the analysis won’t get into unethical players’ hands and used for nefarious purposes?

In particular, let’s be sure that we have thoroughly vented the costs associated with the AI model’s False Positives (accusing an innocent person of child pornography) and False Negatives (missing people who are guilty of child pornography). That is the focus of Part 2!

AI literacy starts by understanding how an AI model works (See Figure 1).

Figure 1: “Why Utility Determination Is Critical to Defining AI Success“

AI models learn through the following process:

1. The AI Engineer (in very close collaboration with the business stakeholders) defines the AI Utility Function, which are the KPIs against which the AI model’s progress and success will be measured.
2. The AI model operates and interacts within its environment using the AI Utility Function to gain feedback in order to continuously learn and adapt its performance (using backpropagation and stochastic gradient descent to constantly tweak the models weights and biases).
3. The AI model seeks to make the “right” or optimal decisions, as framed by the AI Utility Function, as the AI model interacts with its environment.

Bottom-line: the AI model seeks to maximize “rewards” based upon the definitions of “value” as articulated in the AI Utility Function (Figure 3).

Figure 2:  “Will AI Force Humans to Become More Human?”

To create a rational AI model that understands how to make the appropriate decisions, the AI programmer must collaborate with a diverse cohort of stakeholders to define a wide range of sometimes-conflicting value dimensions that comprise the AI Utility Function.  For example, increase financial value, while reducing operational costs and risks, while improving customer satisfaction and likelihood to recommend, while improving societal value and quality of life, while reducing environmental impact and carbon footprint.

Defining the AI Utility Function is critical because as much credit as we want to give AI systems, they are basically dumb systems that will seek to optimize around the variables and metrics (the AI Utility Function) that are given to them.

To summarize, the AI model’s competence to take “intelligent” actions is based upon “value” as defined by the AI Utility Function.

One of the biggest challenges in AI Ethics has nothing to do with the AI technology and has everything to do with Confirmation Bias.  AI model Confirmation Bias is the tendency for an AI model to identify, interpret, and present recommendations in a way that confirms or supports the AI model’s preexisting assumptions. AI model confirmation bias feeds upon itself, creating an echo chamber effect with respect to the biased data that continuously feeds the AI models. As a result, the AI model continues to target the same customers and the same activities thereby continuously reinforcing preexisting AI model biases.

As discussed in Cathy O’Neil’s book “Weapons of Math Destruction”, the confirmation biases built into many of the AI models that are used to approve loans, hire job applicants, and accept university admissions are yielding unintended consequences that severely impact individuals and society. Creating AI models that overcome confirmation biases takes upfront work and some creativity.  That work starts by 1) understanding the costs associated with the AI model’s False Positives and False Negatives and 2) building a feedback loop (instrumenting) where the AI model is continuously-learning and adapting from tis False Positives and False Negatives.

Instrumenting and measuring False Positives – the job applicant you should not have hired, the student you should not have admitted, the consumer you should not have given the loan – are fairly easy because there are operational systems to identify and understand the ramifications of those decisions.  The challenge is identifying the ramifications of the False Negatives.

“In order to create AI models that can overcome model bias, organizations must address the False Negatives measurement challenge. Organizations must be able to 1) track and measure False Negatives to 2) facilitate the continuously-learning and adapting AI models that mitigates AI model biases.”

The instrumenting and measuring of False Negatives – the job applicants you did not hire, the student applicant you did not admit, the customer to whom you did not grant a loan – is hard, but possible.  Think about how an AI model learns – you label the decision outcomes (success or failure), and the AI model continuously adjusts the variables that are predictors of those outcomes.  If you don’t feedback to the AI model its False Positives and False Negatives, then the model never learns, never adapts, and in the long run misses market opportunities. See my blog “Ethical AI, Monetizing False Negatives and Growing Total Address” for more details.

Organizations need a guide for creating AI models that are transparent, unbiased, continuously-learn and adapt, and exist in support of societal laws and norms.  That’s the role of the Ethical AI Application Pyramid (Figure 3).

Figure 3: The Ethical AI Application Pyramid

The Ethical AI Application Pyramid embraces the aspirations of Responsible AI by ensuring the ethical, transparent, and accountable application of AI models in a manner consistent with user expectations, organizational values and societal laws and norms.  See my blog “The Ethical AI Application Pyramid” for more details about the Ethical AI Application Pyramid.

AI run amuck is a favorite movie topic.  Let’s review a few of them (each of these is on my rewatchable list):

• Eagle Eye: An AI super brain (ARIIA) uses Big Data and IOT to nefariously influence humans’ decisions and actions.
• I, Robot: Way cool looking autonomous robots continuously learn and evolve empowered by a cloud-based AI overlord (VIKI).
• The Terminator: An autonomous human killing machine stays true to its AI Utility Function in seeking out and killing a specific human target, no matter the unintended consequences.
• Colossus: The Forbin Project: An American AI supercomputer learns to collaborate with a Russian AI supercomputer to protect humans from killing themselves, much to the chagrin of humans who are intent on killing themselves.
• War Games: The WOPR (War Operation Plan Response) AI system learns through game playing that the only smart nuclear war strategy is “not to play”.
• 2001: The AI-powered HAL supercomputer optimizes its AI Utility Function to accomplish its prime directive, again no matter the unintended consequences.

There are some common patterns in these movies – that AI models will seek to optimize their AI Utility Function (their prime directive) no matter the unintended consequences. 

But here’s the real-world AI challenge: the AI models will not be perfect out of the box.  The AI models, and their human counterparts, will need time to learn and adapt. Will people be patient enough to allow the AI models to learn? And do normal folks understand that AI models are never 100% accurate and while they will improve over time with more data, models can also drift over time as the environment in which they are working changes? And are we building “transparent” models so folks can understand the rationale behind the recommendations that AI models make?

These questions are the reason why Data and AI literacy education must be a top priority if AI is going to reach its potential…without the unintended consequences…

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Opportunities and Risks of Foundation Models

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Data Science Trends of the Future 2022

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Data Science is an exciting field for knowledge workers because it increasingly intersects with the future of how industries, society, governance and policy will function. While it’s one of those vague terms thrown around a lot for students, it’s actually fairly simple to define.

Data science is an interdisciplinary field that uses scientific methods, processes, algorithms and systems to extract knowledge and insights from structured and unstructured data, and apply knowledge and actionable insights from data across a broad range of application domains. Data science is thus related to an explosion of Big Data and optimizing it for human progress, machine learning and AI systems.

I’m not an expert in the field by any means, just a futurist analyst, and what I see is an explosion in data science jobs globally and new talent getting into the field, people who will build the companies of tomorrow. Many of those jobs will actually be in companies that do not exist yet in South and South-East Asia and China.

Data science is thus where science meets an AI, a holy grail for career aspirants and students alike. Data science continues to evolve as one of the most promising and in-demand career paths for skilled professionals. Today, successful data professionals understand that they must advance past the traditional skills of analyzing large amounts of data, data mining, and programming skills. 

This article will attempt to outline a brief overview of some of what’s going on and is by no means exhaustive or a final take on the topic. It’s also going to focus more on policy futurism rather than technical aspects of data science, since those are readily covered in our other articles on an on-going basis.

Augmented Data Management

In a future AI-human hybrid workforce, how people deal with data will be more integrated. Gartner sees this as a pervasive trend. For example, augmented data management uses ML and AI techniques to optimize and improve operations. It also converts metadata from being used in auditing, lineage and reporting to powerful dynamic systems.

Essentially augmented data management will enable active metadata to simplify and consolidate architectures and also increase automation in redundant data management tasks. As Big Data optimization takes place, automation will become more possible in several human fields, reducing task loads and creating AI-human architectures of human activity.

Hybrid Forms of Automation

Automation is one of those buzz words but things like RPA are really moving quite swiftly in the evolution of software. To put it another way, in terms of data, this has a predictable path to optimization. High-value business outcomes start with high data quality. But with the scale and complexity of modern data, the only way to truly harness its value is to automate the process of data discovery, preparation and the blending of disparate data. 

This digitally transforms the very way industries function and do their business. You can see this in nearly every sector where efficiency with data is key. Industries such as manufacturing, retail, financial services and travel and hospitality are benefiting from this trend. The retail industry, for example, has undergone multiple pivots in recent years. What happens at the intersection of consumer behavior with something like DeFi and the future of data on the blockchain?

Automation produces human convenience from the consumer side, but also new machine learning systems that become more important in certain industries. The rise of E-commerce, video streaming, FinTech and tons of other meta-trends in business all depend upon this kind of automation and data-optimization processes.

Scalable AI

As data science evolves, AI and machine learning begins to influence every sector. According to Nvidia there are around 12,000 AI startups in the world. This is important to recognize in the 2020s. There’s an AI explosion of potential that will lead to scalable AI and behavior modification at scale in humans adjusting to this new reality.

The new kinds of capitalism around scalable AI can be called augmented surveillance capitalism. This is important because the way we relate to data is transformative. The Internet of Things becomes embedded in all human systems and activities.

Big Data and “small data” unite and as AI integrates with many new and old aspects of society, something new emerges in how we are able to monitor the flow of data in real time and predict for outcomes instantaneously. It doesn’t just create a more connected society, but a living lens into the data of everything that also stimulates innovation, new companies and, potentially, incredible economic growth. Scalable AI is one of the reasons students get into data science. They realize the end game is beautiful and does improve human existence.

Augmented Consumer Interfaces

When I say ACI I mean how the consumer processes data in the shopping of the future, either online or in a physical store. In the future it’s highly unlikely you will be served by human staff. Instead there will be an AI-agent that you relate to or a new form of the interface of the exchange, such as buying and reviewing products in VR, getting a product overview synopsis audially in your ear buds or other augmented consumer interfaces.

The rise of the augmented consumer takes many forms, from AR on mobile to new methods of potential communication such as a Brain-Computer Interface (BCI). All of these interfaces have consumer, retail and global implications for the future of AI in consumerism.

As companies like Facebook, Microsoft and Amazon race to create a metaverse for the workplace or retail space, even what we think of Zoom meetings or E-commerce interfaces of today will be replaced by new augmented consumer interfaces. Things like a video meeting or an E-commerce platform front page may become outdated. This is also because the data they provide may be sub-optimal for the future of work productivity and data based consumerism.

Essentially VR, IoT, BCI, AR, AI speakers, AI agents, chatbots and so forth all evolve into a new paradigm of augmented consumer interfaces where artificial intelligence is the likely intermediary and people live in the real world but also in a corporate and retail metaverse with different layers. As Facebook tries to bring the workplace into its conception of the metaverse, other companies will create new interfaces for ACI efficiency.

Amazon recently announced that it is planning to open large physical retail stores in the United States that will function as departments stores and sell a variety of goods including clothes, household items and electronics. Yet you can expect Amazon’s various physical spaces to also have more consumer data capture to incentive purchasing or even completely automate the store such as famously their smallish Amazon Go Stores. A more seamless shopping experience creates new expectations for the consumer that finally leads to new augmented consumer interfaces, that will just become the new normal.

AI-As-a-Service Platforms

With data science growing and machine learning evolving, more B2B and AI-as-Service platforms and services will now become possible. This will gradually help democratize artificial intelligence expertise and capabilities so even the smaller entpreneurs will have access to incredible tools.

Platforms like Shopify, Square, Lightspeed and others are heading this direction to enable new small businesses optimized with AI to grow faster. Meanwhile more bigger technology firms are entering the B2B market with their own spin on AI products that other businesses might need.

China’s ByteDance really opened BytePlus which enables a wide variety of intelligent platform services for other businesses. Market leaders like Google, Baidu and Microsoft and Amazon in the Cloud have a significant ability to produce AI-As-a-Service at scale for customers in new ways that are always evolving for the news of industry. The NLP-As-a-Service firms and their progress is also an example of this movement.

The AI-as-a-Service platform growth in the 2020s is as I see it one of the biggest growth curves in data science for years to come generally speaking.

The Democratization of AI

As data science talent becomes more common in the world in highly populated countries, a slight re-balancing of the business benefits takes place in more countries. The democratization of AI will take many decades, but eventually data science will be more equally distributed around the world leading to more social equality, wealth equality, access to business and economic opportunities and AI for Good. We are however a long ways away from this goal.

Wages for data science and machine learning knowledge workers are vastly different in different regions of the planet. A company in Africa or South America may not have the same access to data capabilities, AI and talent as one in “more developed” regions of the Earth. This however will slowly change.

Democratization is the idea that everyone gets the opportunities and benefits of a particular resource. AI is currently fairly centralized however ideas of decentralization, especially in finance, crypto and blockchain may trickle down into how AI is eventually managed and distributed more fairly. AI as a tool is a resource of significant national importance and not all countries have similar per capita budgets to invest in it, in its R&D and in producing companies that harness it the best.

Yet for humanity, the democratization of AI is one of the most important collective goals of the 21st century. It is pivotal if we want to live in a world where social justice, wealth equality and opportunity for all matters in fields such as healthcare, education, commerce and fundamental human rights in an increasingly technological and data-driven world.

The most basic element of the democratization of AI is however that anybody anywhere could become a student of data science and has access to becoming a programmer or a knowledge worker who works with data, machine learning, AI and related disciplines. It all starts with access to education in this sense.

Improved Data Regulation By Design

If datascience is fueling a world full of data, analytics, predictive analytics and Big Data optimization the way we handle data needs to improve and this means better cybersecurity, data privacy protection and a whole range of things.

China’s BigTech regulatory crackdown also has to do with better data regulation. For instance just recently in August, 2021 China has passed the Personal Information Protection Law (PIPL), which lays out for the first time a comprehensive set of rules around data collection.

In augmented surveillance capitalism human rights need to be implemented by design and that means better protection of consumer data especially as AI moves into healthcare, sensitive EMR and patient data. Overall the trend of Data privacy by design incorporates a safer and more proactive approach to collecting and handling user data while training your machine learning model on it.

How we move and build in the Cloud also needs scrutiny from a policy regulation standpoint. Currently data science is moving faster than we can regulate data privacy and make sure the rights of individuals, consumers and patients are respected in the process. Even as data science and Big Data explode, rule of law needs to be maintained otherwise our AI systems and data architecture could lead to rather drastic consequences.

Top Programing Language of Data Science is Still Python

Even though I do not code I’m pretty interested in how programming languages are used and evolving. Python continues to be a defacto winner here. Data science and machine learning professionals have driven adoption of the Python programming language.

Python’s libraries, community and support system online is just incredible to behold and displays how data science is a global community of learners and practitioners. This fosters the collaborative spirit of the internet towards improved data and AI systems in society.

Python as such, is not just a tool but it’s also a culture. Python comes stacked with integrations for numerous programming languages and libraries and is thus the likely entry point of getting into data science and the AI world as a whole. What we have to realize is the programming culture is agile but also highly collaborative making it incrementally easier for new programmers and data science talent to emerge.

Cloud Computing with Exponential AI

The turning point of companies moving into the Cloud is hard to calculate in how exponential it’s leading the emergence of the AI revolution in the 21st century. This is frankly leading to an astonishing array and improvement in the services offered by Cloud computing providers.

Think of the impact of AWS marketplace and their equivalents in Azure, Google, Alibaba, Huawei and others and you get some scope of how Cloud computing and machine learning are in-it-together.

This creates countless jobs, adds value and harnesses the power of data science, machine learning and Big Data for businesses all around the world. The intersection of the Cloud, datascience and AI is truly not just a business but a technological convergence point, it’s own kind of micro singularity if you will.

The depth of the features offered by AWS and Azure is continually self-optimizing to such an extent that something like Google Cloud could harness quantum computing for a whole new era of data science, AI and predictive analytics and value creation.

Cloud automation, Hybrid Cloud solutions, Edge Intelligence, and so much of the technical stuff happening in data science today happens in the Cloud and its happy marriage with AI and datascience services. The increased use of NLP in business, quantum computing at scale, next-generation reinforcement learning – it’s all possible because the Cloud is evolving at such a rapid pace.

The Analytics Revolution

For data to become truly ubiquitous in society and business one thing is needed. The automation and accessibility that is made possible when analytics becomes a core business function. Better data and analytics means better business decisions. This is a bit what Square enables for small businesses with augmented analytics at the point of sale or real-time analysis of a small businesses finances that ensure their long-term survival and more rapid adaptation than would be possible without that data, analytics and insights.

The analytics revolution allows data to become actionable in society in-real time and unlocks the true value of datascience for merchants, businesses, smart cities, countries and public institutions at scale. Imagine if our healthcare, education and governance were all embodied analytics and data like our entertainment, E-commerce and mobile systems to today? Imagine the human good that would accomplish.

When data analytics becomes a core of a business, the value it unlocks achieves gains at every stage of its business cycle. Many businesses that were once used to approach analytics as a nice-to-have support function , gradually now embracing it as mission critical. This is what FinTech can do for consumers in a way that Bank cannot and eventually it’s a disruptive force. The analytics revolution is datascience at work in society with AI driving new value chains and optimizing existing ones.

The Impact of Natural Language Processing in the Future of Data Science

In 2021 there’s been a lot of “bling” about NLP systems of scale. It’s not just happening at OpenAI with GPT-3, it’s occurring all over the world. NLP and conversational analytics also will one day allow for AI to be more human-like and this opens up the door for a more living world of machine learning that’s not just routine algorithms, but more personalized and humanized.

Language is the key for people, so NLP will make AI more life-like. Your smart speaker and smart assistance, chat bots and automated customer service are only going to get smarter. That smart OS in movies such as Her, will soon be closer to being a reality. AI as a form of human companionship will exist within our lifetimes, probably a lot sooner.

NLP has hugely helped us progress into an era where computers and humans can communicate in common natural language, enabling a constant and fluent conversation between the two. Voice based search is becoming more common, smart appliances have voice options, IoT and NLP will have babies, it won’t be about redundant chatbots and digital assistants you never use, but about actionable NLP in the real-world that work.

While I believe using an AI assistant to help you code is a bit far-fetched it’s interesting to see what Microsoft is doing with OpenAI and GitHub. OpenAI’s codex is a good water cooler topic, and the AI-human hybrid buddy system may eventually be coming to the future of coding and programming. One thing is for certain in 2021, NLP in datascience is a hot topic full of brimming potential for knowledge workers and keen entpreneurs.

When Microsoft acquired Nuance, you knew NLP was coming to Healthcare at scale. Nuance is a pioneer and a leading provider of conversational AI and cloud-based ambient clinical intelligence for healthcare providers. The synergies of NLP companies and the Cloud are obvious.

The applications of NLP in the Cloud and in society is one of the greatest explosions of AI entering the human world we will perhaps ever see. What happens to society when AI becomes more human-like and capable of conversation? On Tesla’s AI day, they declared they were building an AI humanoid called Optimus. I suppose it will have pretty sophisticated NLP capabilities.

Conclusion and Future Ideals of the Impact of Data Science 

I hope you enjoyed the article and I tried to bring a few fresh points of view to the wonderful future of datascience and highlight how mission critical it is for a new wave of talented knowledge workers to enter data science, programming and machine learning to help transform human systems for the uplifting of the quality of life for all of us on planet Earth. 

This article was 2,929 words. 

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Has the Pandemic Accelerated AI in Healthcare?

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Introduction 

While the pandemic has spurred digital transformation, even a corporate metaverse debate about the future of remote work, AI was not invaluable during the pandemic in fighting against Covid-19 directly. AI should have been able to warn us that a pandemic was coming, but it didn’t. Those few weeks of uncertainty were very costly in how countries prepared for what was to come.

Still AI in healthcare has made many little gains during the last two years, many of which have not been well publicized. As for a warning system of AI for the next pandemic, a recently announced early warning system designed by MedShr Insights may have the capability to predict pandemics. The capability fetched the technology third place honor in the Trinity Challenge and a prize of $660,000.

AI Shows Promise in Early Diagnosis and Detection 

AI has improved in reading various medical scans and tests, to catch what humans miss. Early detection of diseases, dementia and many other conditions and hyper-personalizing the patient experience are certainly the abode of artificial intelligence’s impact on the future of healthcare. AI in pharma and drug discovery also has grown in leaps and bounds.

AI in healthcare is also somewhat controversial. Back in 2019, a group of healthcare specialists created an AI-based system that can predict the risk of premature death caused by chronic disease. For urgent situations where ICUs are full and doctors need to decide which Covid-19 patients to treat first or give priority to, AI should be making the call, as it’s very stressful for people to make those kinds of choices.

Future of AI in Healthcare and Need for Better AI Ethics Highlighted 

AI will certainly be implicated in triage efficiency too, as well as preventative healthcare. Conditions like Long-covid (Long Haulers) will likely enable AI to learn which symptoms are most likely to lead to temporary disability. AI will also be used in the advent of biotechnology, human augmentation and medical ethics and the WHO created important guidelines around the use of AI in Healthcare. I find this fascinating as I think a lot about AI ethics.

What I especially like about the WHO guidelines and report about AI’s impact on healthcare is moderation vs. the hype. Their new ethics guidance cautions against overestimating the benefits of technology. This is important to realize as an abundance of headlines and research doesn’t mean AI is impacting the current reality of healthcare all that much.

The new guidance, Ethics & Governance of Artificial Intelligence for Health, is the result of two years of consultations held by a panel of international experts appointed by the WHO. While the WHO was possibly partly incompetent in certain aspects of the early stage pandemic, their awareness and attempt to make rules around AI’s use for a humanitarian sense is really one of the better models we have.

The World Health Organizations Principles Around AI

What follows is their quote directly from here.

Ultimately, guided by existing laws and human rights obligations, and new laws and policies that enshrine ethical principles, governments, providers, and designers must work together to address ethics and human rights concerns at every stage of an AI technology’s design, development, and deployment. 

Six principles to ensure AI works for the public interest in all countries

To limit the risks and maximize the opportunities intrinsic in the use of AI for health, the WHO provides the following principles as the basis for AI regulation and governance:

1. Protecting human autonomy: In the context of health care, this means that humans should remain in control of health-care systems and medical decisions; privacy and confidentiality should be protected, and patients must give valid informed consent through appropriate legal frameworks for data protection.

2. Promoting human well-being and safety and the public interest. The designers of AI technologies should satisfy regulatory requirements for safety, accuracy and efficacy for well-defined use cases or indications. Measures of quality control in practice and quality improvement in the use of AI must be available.

3. Ensuring transparency, explainability and intelligibility. Transparency requires that sufficient information be published or documented before the design or deployment of an AI technology. Such information must be easily accessible and facilitate meaningful public consultation and debate on how the technology is designed and how it should or should not be used.

4. Fostering responsibility and accountability. Although AI technologies perform specific tasks, it is the responsibility of stakeholders to ensure that they are used under appropriate conditions and by appropriately trained people. Effective mechanisms should be available for questioning and for redress for individuals and groups that are adversely affected by decisions based on algorithms.

5. Ensuring inclusiveness and equity. Inclusiveness requires that AI for health be designed to encourage the widest possible equitable use and access, irrespective of age, sex, gender, income, race, ethnicity, sexual orientation, ability or other characteristics protected under human rights codes.

6. Promoting AI that is responsive and sustainable. Designers, developers and users should continuously and transparently assess AI applications during actual use to determine whether AI responds adequately and appropriately to expectations and requirements. AI systems should also be designed to minimize their environmental consequences and increase energy efficiency. Governments and companies should address anticipated disruptions in the workplace, including training for health-care workers to adapt to the use of AI systems, and potential job losses due to use of automated systems.         

Human Rights in the Metaverse AI-of-Everything World?

In the spirit of digital transformation a lot of progress is actually by-passing certain aspects of our human rights where legal regulation isn’t taking place. It would be a pity if AI’s impact on healthcare were one of these areas where data protection, privacy and the right to personal choice was not implemented in an orderly fashion. Since we know that companies such as Google, Amazon, Apple and others are getting aggressively into healthcare we must be vigilant to maintain a high ethical code of conduct around the impact of AI in healthcare.

While digital transformation has flourished during the pandemic, I don’t think we’ve seen a corresponding explosion of AI in healthcare breakthroughs as we have in other fields such as FinTech, the corporate metaverse (e.g. Microsoft Teams), home fitness or even telemedicine itself as a rapidly maturing industry. AI in healthcare is more ubiquitous but slow moving and becoming more active in academic research and medical R&D such as drug discovery in particular.

Benefits outweigh the Risks for AI in Healthcare

Europe appears to be thinking the most of how AI will impact healthcare among global bodies. Earlier this year, European health innovation network, EIT Health launched an AI report from its Think Tank, urging healthcare providers to invest more in AI and tech post-pandemic. Dozens of health tech startups are also utilizing AI in healthcare solutions and they will mature in the 2020s as will biotechnology companies that scale to the mainstream.

Dr Tedros Adhanom Ghebreyesus, WHO Director General, said: “Like all new technology, AI holds enormous potential for improving the health of millions of people around the world, but like all technology it can also be misused and cause harm. Indeed the abuse of medical data and privacy abuses of technology coming closer to the home (as with remote work) brings up many questions as to AI’s impact on the data harvesting of people with special conditions. If the smart home is to become medically proficient with AI, how do the patients know where their data is going? If an E-commerce retailer knows which meds I get delivered or Apple builds an integrated EMR system, that’s a lot of our sensitive data potentially exposed to third parties.

AI will Soon Personalized Healthcare and Get More Personal as Our Intermediary with the World 

Even more likely than us living in a metaverse is AI being embedded in our world, and dealing with our most intimate mental health, social, cognitive, and health related data. What are our human rights in such a world of AI permeating our health related environments? If my FitBit knows about my sleep patterns, will Google use that information to tailor Ads to me with predictive analytics? The reality of an AIoT world are both amazing and a little frightening. The impact of AI on our mental health life is particularly worrisome.

While technology leads to more loneliness in society as mobile time and streaming time cut into human time, will AI help us one day with a fake solution to our technological loneliness? These are some of the very real human questions around the use of AI in healthcare (much of it in the home) in the future. AI in healthcare has huge implications on major developments such as:

• Adventure of the brain-computer interface (BCI)
• AI’s impact on radiology, early detection scans and Big Data to personalize care
• Improving healthcare accessibility and inclusion to underserved populations and the most vulnerable
• Using predictive analytics on medical history related to family history for significant reduction in early detection of ailments
• Bringing Electronic Medical Records (EMR) into the cloud and tracking much more than typical EMR systems do, with more efficiency and embodied artificial intelligence.
• Dealing with large systemic issues like the rising cost of healthcare, the burden of antibiotic resistance, the problems associated with reduced global fertility rates, the rising percentage of the elderly (not to mention the next pandemic).

Conclusion: AI in Healthcare is Just Beginning 

The pandemic has not been aided by AI by and large, but we have a better frame of reference for what the AI of healthcare will entail in the 2020s and 2030s with many new developments related to healthcare.

The AI of Healthcare will have distinct costs and benefits, and medical professionals will increasingly work in an AI-human hybrid system. Medical devices, machines and robotics (including robotic surgery) will take decades to improve and be refined.

Globally in the 2020s, AI in healthcare is just in its infancy. For data scientists and knowledge workers it’s clear machine learning will become more implicated in the years ahead in making clinical decisions with better data. Medical transcriptions and software that helps doctors reduce their task load is already accelerating at a rapid pace.

Several types of AI are already being employed by payers and providers of care, and life sciences companies. AI is just exploring what’s possible to improve and revolutionize healthcare, improve longevity and make healthcare more affordable and accessible to all. While AI contributes to aspects of dystopia in some ways, AI in Healthcare is one of the key ways that AI can help us move to a world that more resembles a utopia. AI’s impact in healthcare is so radically good the entire narrative of ‘AI for Good’ may hinge upon it.

As for knowledge workers in the datascience and machine learning realm, we will need talent to make AI in Healthcare a reality. The quality of life for millions of people may depend upon it. 

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Top 4 Artificial Intelligence Engineer certifications in 2021

With the high rise in demand for talent in the field of Artificial Intelligence (AI), the need for professionals who have expertise in this field has also increased immensely. Worldwide, many organizations are on the lookout for individuals who possess a great skillsets in the field of AI.

This demand gave rise to the artificial intelligence engineer certification program, which is offered by several online learning institutes. If a person wants to enhance their skill set and also stay ahead in the growing populations then doing a certification program in the field of AI is the best choice.

In this article, let’s understand the most affordable and industry-recognized top AI certifications that one can do to jump the career ladder.

Coursera is a world-class learning platform that offers numerous certification programs in various fields. It has partnered with more than 200 leading universities and many firms to give the most affordable, flexible, job-relevant online learning to the people who want to step up in their careers. Among those, there is a certification program called “AI For Everyone.” This certification program offers in-depth knowledge of AI terminologies like neural networks, Machine Learning (ML), deep learning, and data science. This program provides a better understanding of many AI strategies that are helpful for developing ML and data science projects.

Artificial Intelligence Board of America (ARTiBA) provides Artificial Intelligence Engineer(AIE™) certification. The main objective of this certification program is to empower every professional to enrich their career in the field of AI. The AIE™ certification based on the internationally recognized AMDEX™ framework offers an individual to attain knowledge as Subject Matter Experts (SME) as well as rigorous AI learning and industry-relevant exposure.   

In this AI certification program, one can learn the several concepts of ML, supervised and unsupervised learning, Natural Language Processing (NLP), cognitive computing, reinforced learning, and deep learning. This program is self-paced and is ideal for those who are looking to gain better knowledge. One can appear for an exam after 45 days of registration. This is high-level certification best fits those individuals who want to gain an edge in the field of AI engineering.

This artificial intelligence engineer certification program also provides thorough learning and preparing experience by offering a free learning deck that comprises special resources curated by top-level industry experts. It is specially designed to help applicants to develop necessary skills and acquire job-ready capabilities so that they can step up in their career ladder and grab leadership positions.

• Eligibility

Registrants are needed to fulfill specific prerequisites that are based on education as well as professional experience to be eligible for AIE™ certification. Overall, the registrants can be categorized under the following 3 tracks:

Microsoft offers Professional Program Certification in Artificial Intelligence that provides a comprehensive program of study in the field of AI. A person can explore many areas that include the basics of ML, Language, and Communication, Computer Vision, also learn key programming language-Python. The individuals will also have the freedom to opt between the areas related to Computer Vision and Image Analysis or Speech Recognition Systems or Natural Language Processing (NLP) where they can start leveraging data to develop intelligent solutions.

Simplilearn provides an Artificial Intelligence Engineer certification program in collaboration with IBM. This certification program will enhance the skill set of a person and makes them to gain more expertise in the field of Artificial Intelligence. Each individual can master the concepts of data science with python, ML, deep learning, & NLP with the best features from IBM like hackathons, master classes, live sessions, practical labs, ask me for anything sessions & projects. Registrants will also get access to the IBM Cloud Lite account and Simplilearn AI Master’s Certificate that is industry-recognized globally.

If a person is looking for affordable and industry-recognized certification programs in the field of AI. Then the aforementioned certification programs are the best choice for them. They not only enhance your career but also help to grab a growth-driven job with an attractive salary in a well-established company.

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Machine Learning Meets Deep Learning in Autonomous Vehicles

Transcending human perception, today autonomous vehicles or AVs have become a reality. Auto giants such as Tesla, BMW, Google, Volkswagen and Volvo have been the front runners in introducing autonomous transportation to ease the pressure off. Meanwhile, the market growth for AV or autonomous vehicles is estimated to touch 40% of CAGR, annually in the next six years.

Before going further, it would be worth knowing how vehicles can function autonomously. An AV or autonomous vehicle is equipped with a vision system, LIDAR or radar based sensing and multiple neural networks trained with data and machine learning algorithms. The multi-layer neural network enables the autonomous cars to identify objects and recognize them. This processed data through machine learning algorithms learns about the objects and enables the vehicle to figure out the next move based on the environment. It may sound simple, however, from object detection and recognition to processing of next move, AVs rely on the neural network data, 360 degree view of the environment using 3D point cloud segmentation and object detection using computer vision.

AV levels and ADAS or Advanced Driver Assist System

In some countries like the US, self driving vehicles are regulated as per the technical specification they are equipped with. ADAS or Advanced Driver Assist System, is a driving assistant support mechanism and is the key indicator of level of autonomy an AV is equipped with. There are following autonomy levels delineated under the ADAS:

• Level 1 (DA or driver assistance) includes adaptive cruise control, emergency brake assist, automatic emergency brake assist, lane-keeping, and lane centering.
• Level 2 (POA or partial operation automation) includes highway assist, autonomous obstacle avoidance, and autonomous parking.
• Level 3 (CA or conditional automation) to include highway driving, driver initiated lane change, automated valet parking.
• Level 4 (AOA or advanced operation automation) sustainable and operational design; limited implementation of DDT or dynamic driving task.
• Level 5 is when the vehicle is fully autonomous; no human intervention.

Deep learning: Algorithms, neural network mesh with ADAS inclusion

Object detection, recognition, image localization and prediction of the next movement form the core when it comes to autonomous vehicles. The localization remains a challenging task in autonomous vehicles which enables them to understand its own positioning on the ground. This challenge is dealt with satellite based navigation systems and inertial navigation systems. The ADAS works for long range detection while CNNs play a critical role in lane detection, pedestrian detection and redundant object detection as well.

The CNNs or convolutional neural network are powered by machine learning algorithms that work and process the sensor data in real-time to produce actionable steps of the vehicle. This is an extremely crucial and data-intense operation, and requires fast execution. Therefore, most autonomous vehicles are built with specific hardware requirements based on the multi-format data interaction and simultaneous processing. The hardware requirements include GPU, TPU, FPGA for training and deployment to make the vehicle functional

                                                                       Image credit: researchgate

Autonomous vehicles employ deep learning and there is a concrete reason for this. AVs function on end-to-end learning and everything takes place in real-time, hence the data processing must be at lightning fast speed. The method of processing images for example, applied in the vehicles, wherein, the camera images are directly processed into CNN which reproduces steering angle as output. The deep learning (mesh) is performed by the massive technology structure that enables the vehicle to perceive, plan, coordinate and control. The overall movement of the vehicle encompasses these:

• For perception: Localization or environmental perception
• For plan: Mission, behavior and motion planning
• For control: Path or trajectory tracking       

Combined with machine learning algorithms, the data utilized for processing via deep learning pass through algorithms like pattern recognition methods – SVM or support vector machine with histogram of oriented gradients and PCA or Principle Component Analysis. The clustering algorithm is also utilized to find out the most relevant and appropriate imagery to understand the environment. While, the decision matrix algorithm – Adaboost is used for making overall prediction and decision of car’s movement.

End note

The autonomous vehicles are under trial for a few years. An increased focus on making the movement of the vehicle safe and efficient around the cities across the world can be seen. Pertinently, localization has been the most crucial challenge to work on and the recent development and advancement in the concept of SLAM (Simultaneous localization and mapping) has significantly addressed some of the localization challenges. The perception of the environment of an autonomous vehicle has undergone many changes in the past couple of years and still continues to leverage the sensor data and semantic data of the topology in order to perform consistently as per the shifting environment. With millions of accidents happening due to human error, AVs are a revolutionary concept for mobility in the smart cities or cities of the future. They can contribute significantly in reducing human dependency and avert human errors on the road; and also reduce stress and congestion on roads. Electric AVs will aid in reducing the carbon emissions as well. The introduction of autonomous vehicles will also help in going to the next level and achieving

Vehicle-to-Vehicle communication. Google’s self-driving cars program, Waymo, has recorded the most successful run in the autonomous vehicles category, until now. More is expected in the AV domain in the coming years, something to wait and watch out for.

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Cybersecurity Concerns in the Age of Hybrid Workplaces

Even as the threat of COVID-19 eventually normalizes in our post-pandemic environment, many of the habits and changes we made will likely stay. One of those is hybrid workspaces.

 A hybrid workplace or workspace is a flexible system that allows workers to shift between onsite and offsite work. According to recent data, 65 percent of employees want a hybrid workspace moving forward. This is understandable as working remotely means employees no longer have to deal with the stress and cost of a long commute and can work at their own pace. Supervisors are also embracing the idea of a hybrid workplace because the pandemic proved that employees could be as productive, if not more when working at home.

 It seems like the ideal solution for everyone. However, cybersecurity experts have raised concerns about the hybrid workplace model. 

The risks of remote work

In a traditional office setting, implementing cybersecurity measures such as protection from DDOS attacks is easy. However, in a hybrid workspace, things become a bit more complicated. Most enterprises have a secure network that employee devices can connect to, ensuring some degree of protection. The office devices are also equipped with top-of-the-line antivirus software and are monitored by the I.T. team.

 However, your employees’ home networks and devices may not have this level of security, leaving them vulnerable to potential attacks. Some employees may even be accessing public networks like cafe or library routers, which could jeopardize the company if their device contains sensitive information. Besides this, there’s also the increased risk of employees losing work devices. Some companies provided work laptops or tablets for their employees to bring home. While these devices helped maintain productivity throughout the lockdowns, they are now an additional weak link to the already fragile cybersecurity chain. More persistent cybercriminals now have the option to steal these devices and extract company secrets from them.

 There is also the concern of slower emergency responses. When working onsite, any emergency is quickly made apparent to the supervisors, and the I.T. department as they’re often a few steps away. However, with remote work, you’ll have to call or email to report an incident, and there’s a chance the concerned parties may not be available to address it immediately. This is devastating because even a few seconds can spell the difference between a close call and absolute catastrophe in a crisis like this. 

Cyberattacks are on the rise

During the pandemic, many companies adopted cloud services to facilitate the storage and transfer of data among remote employees. Along with this trend, analysts noticed a 140 percent increase in RDP attacks and a boom in phishing and malware cases. This correlation shows that cybercriminals are aware of the cybersecurity gaps that come with remote and hybrid workspaces and are doing their best to exploit them while companies and experts scramble to find ironclad solutions. 

Securing a hybrid workplace

Unfortunately, no pre-packaged solution can provide a hundred percent guarantee that you won’t fall victim to a cyberattack. However, following the provided steps will at least minimize the risk. 

• Implement strong passwords and activity timers

Whether it’s the device, domain, applications, or other office network service, ensure that strong passwords are in place. Use a mixture of symbols, numbers, uppercase, and lowercase letters. Cybersecurity experts advise never to use the same password and to change it every 60-90 days. In addition, you can improve security by implementing two-factor authentication where you can.

Besides passwords, an additional security measure is implementing activity timers. This will automatically log out a user who has been idle for a certain time. This ensures that users don’t accidentally stay logged into the system and leave it vulnerable to infiltration. 

• Use full disk encryption

Disk encryption ensures that even if a work device were stolen or lost, the information it contains wouldn’t be accessible to hackers. There are various tools available for this purpose, but use one that provides the highest-level security so that even a sophisticated decoding algorithm can’t crack the code. 

• Set access limits

Not all information should be accessible on any remote device by any employee. This ensures some degree of control over the most sensitive company data. Ideally, access to the internal network should only be done on an onsite device monitored by the I.T. department. 

• Educate your employees

Humans are the weakest link in a cybersecurity plan. Even if the system in place is the best current technology has to offer, all it takes is one person’s mistake for it to all come crashing down. Teach your employees the security protocols and the importance of adhering to them. Deliver the information in a way that even those who aren’t tech-savvy will understand. Here are a few key reminders each employee must abide by: 

• Never write down login credentials: It seems obvious, but you’d be surprised how many people keep passwords on post-its, notebooks, or their phones. Understandably, multiple strong passwords are difficult to remember, but use secure password managers instead of writing them down.
• Never connect to public wi-fi on your work devices: There are many risks in connecting to public wi-fi, from hackers intercepting your data to stealing passwords. Even with a VPN, it’s still not recommended.
• Never leave your work device unattended: If your work device is not in use, ensure it is secure, either by keeping it in a locked drawer or room. If you’re bringing it to another location like a library or cafe, never leave it on the table. Some employees have the unfortunate habit of letting their family use their work devices. Even if they don’t have malicious intent, they may unknowingly put the company at risk by clicking on suspicious ads or installing a virus. 

• Partner with cybersecurity experts

Like how you would hire security guards to protect your physical office, it’s best to contract professional-level services to ensure your business’s safety. Most companies were content with basic cybersecurity plans, but if you’re planning to make your workplace thoroughly hybridized, it’s best to upgrade your security to plug all the gaps in remote work. 

Moving forward

While remote work is not new, this is the first time it’s being implemented on such a large scale, and the fact that many companies were not prepared for this situation only puts them even more at risk. There was no time to train employees to conduct remote work without compromising company secrets and no time to prepare the appropriate infrastructure to maintain secure data transfer. 

Fortunately, companies have started investing in tighter cybersecurity measures to complement hybrid workplaces. With this, employees can enjoy greater flexibility without additional risk to the company. In addition, an increased interest in the hybrid workplace means that more funding is being funneled into research focused on strengthening remote security. With these changes, all our worries regarding remote work may soon be a thing of the past.

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