SigmaWay Blog

Artificial Intelligence (AI) is reshaping industries, with Machine Learning (ML) and Deep Learning (DL) standing out as its most influential technologies. ML involves algorithms that learn patterns from data to make decisions, such as spam filters identifying unwanted emails based on labeled examples. Its adaptability makes ML widely useful in fields like finance and healthcare, where it powers predictive analytics to forecast trends and outcomes.

Deep Learning, a subset of ML, uses neural networks to automatically extract and learn features from large datasets. This makes it highly effective for complex tasks such as image and speech recognition. For instance, DL enables facial recognition systems to identify individuals with remarkable precision and supports innovations like autonomous vehicles and advanced medical diagnostics.

While ML excels in handling diverse applications with moderate complexity, DL’s computational power is better suited for cutting-edge problems requiring deep insights. Together, these technologies are driving AI’s evolution, transforming industries and expanding the possibilities of automation and innovation.

In the dynamic landscape of digital marketing, understanding user behavior is vital for creating impactful campaigns. User clicks provide a wealth of valuable data, and Machine Learning (ML) acts as a powerful tool to interpret and harness this information. By utilizing ML algorithms, digital marketers can analyze click patterns to develop highly targeted strategies, ensuring maximum efficiency and optimal results.

One of the standout applications of ML in digital marketing is its ability to personalize content recommendations. Through predictive modeling, ML can anticipate what a user is likely to engage with next, enabling marketers to deliver tailored suggestions that align with individual preferences. This not only enhances the user experience but also amplifies the effectiveness of marketing initiatives. Tools like Predictive Analytics further refine this process by analyzing past click data to forecast future user behavior, helping businesses target their audiences with precision.

ML also significantly improves ad optimization and audience segmentation. By examining click behavior, it identifies the most effective ads, ensuring they reach the right audience with maximum impact. Additionally, ML can group users with similar interests based on their behavior, allowing marketers to design personalized campaigns. Notable examples include Netflix recommending shows based on viewing history, Amazon suggesting products based on past activity, and Google Ads displaying highly relevant ads. These applications demonstrate how ML is transforming digital marketing into a smarter, more personalized, and highly efficient domain, helping businesses forge stronger connections with their users.

Big data analytics in the pharmaceutical industry is changing drug development and delivery. Enormous volumes of data enable a pharmaceutical company to make more informed decisions and work much more efficiently.

Predictive analytics facilitates demand estimation and proper supply chain management, hence ensuring drugs are always available. Personal medicine ensures that treatment is given according to the individual patient's condition and is therefore effective. Real-time analytics in clinical trials run the changes with patients, and on time, spot any emerging problems, hence fast-tracking drug development. Big data also plays a very key role in discovering side effects and improving the safety of drugs.

In the future, as data analytics further develops, it could be that, by the big strides ahead in the treatment of pharmaceuticals, there are more effective treatments and better health outcomes for all.

The rise of AI and GPT technologies presents significant ethical and security challenges. A major issue is bias in AI systems, where algorithms may reflect and perpetuate societal prejudices, leading to unfair treatment in areas like hiring or criminal justice. Additionally, misinformation generated by AI-powered systems poses risks, as GPT models can produce convincing but false or misleading content.

Privacy concerns are another challenge, with AI being used to collect and analyze personal data without consent. Moreover, AI-generated deepfake videos and voice impersonation pose risks to credibility and authenticity, enabling fraud and misinformation by mimicking real individuals' faces and voices. In a broader sense, the potential for job displacement due to automation raises economic and social concerns. Let’s look at some more challenges:

Unjustified Actions: Algorithmic decision-making often relies on correlations without establishing causality, which can lead to erroneous outcomes. Inauthentic correlations may be misleading, and actions based on population trends may not apply to individuals. Acting on such data without confirming causality can cause inaccurate and unfair results.

Opacity: This issue refers to AI's decision-making being hidden or unintelligible. This opacity stems from complex algorithms and data processes being unobservable and inscrutable, making AI unpredictable and difficult to control. Transparency is essential but not a simple solution to AI-related ethical issues.

Bias: AI systems reflect the biases of their designers, contradicting the idea of unbiased automation. Development choices embed certain values into AI, institutionalizing bias and inequality. Addressing this requires inclusivity and equity in AI design and usage to mitigate these biases.

Gatekeeping: AI’s personalization systems can undermine personal autonomy by filtering content and shaping decisions based on user profiles. This can lead to discriminatory pricing or information bubbles that restrict decision-making diversity. Third-party interests may override individual choices, affecting user autonomy.

Complicated Accountability: As AI spreads decision-making, it diffuses responsibility. Developers and users might shift blame, complicating responsibility for unethical outcomes. Automation bias increases reliance on AI outputs, reducing accountability in complex, multi-disciplinary networks. Moreover, the notion that engineers and software developers hold “full control” over each aspect of an AI system is usually precarious.

Ethical Auditing: Auditing AI systems is crucial for transparency and ethical compliance. Merely revealing the code does not ensure fairness; comprehensive auditing, through external regulators or internal reporting, helps identify and correct issues like discrimination or malfunction. This process is essential for AI systems with significant human impact.

Addressing these issues requires transparency, improved regulations, and responsible AI development practices. Bias in AI can be mitigated by diverse training datasets, while stricter policies can limit the misuse of generated content. Collaboration between tech companies, policymakers, and ethicists is crucial to ensure the responsible and ethical use of AI in society.

The rise of AI and GPT technologies presents significant ethical and security challenges. A major issue is bias in AI systems, where algorithms may reflect and perpetuate societal prejudices, leading to unfair treatment in areas like hiring or criminal justice. Additionally, misinformation generated by AI-powered systems poses risks, as GPT models can produce convincing but false or misleading content.

Privacy concerns are another challenge, with AI being used to collect and analyze personal data without consent. Moreover, AI-generated deepfake videos and voice impersonation pose risks to credibility and authenticity, enabling fraud and misinformation by mimicking real individuals' faces and voices. In a broader sense, the potential for job displacement due to automation raises economic and social concerns. Let’s look at some more challenges:

Unjustified Actions: Algorithmic decision-making often relies on correlations without establishing causality, which can lead to erroneous outcomes. Inauthentic correlations may be misleading, and actions based on population trends may not apply to individuals. Acting on such data without confirming causality can cause inaccurate and unfair results.

Opacity: This issue refers to AI's decision-making being hidden or unintelligible. This opacity stems from complex algorithms and data processes being unobservable and inscrutable, making AI unpredictable and difficult to control. Transparency is essential but not a simple solution to AI-related ethical issues.

Bias: AI systems reflect the biases of their designers, contradicting the idea of unbiased automation. Development choices embed certain values into AI, institutionalizing bias and inequality. Addressing this requires inclusivity and equity in AI design and usage, to mitigate these biases.

Gatekeeping: AI’s personalization systems can undermine personal autonomy by filtering content and shaping decisions based on user profiles. This can lead to discriminatory pricing or information bubbles that restrict decision-making diversity. Third-party interests may override individual choices, affecting user autonomy.

Complicated Accountability: As AI spreads decision-making, it diffuses responsibility. Developers and users might shift blame, complicating responsibility for unethical outcomes. Automation bias increases reliance on AI outputs, reducing accountability in complex, multi-disciplinary networks. Moreover, the notion that engineers and software developers hold “full control” over each aspect of an AI system is usually precarious.

Ethical Auditing: Auditing AI systems is crucial for transparency and ethical compliance. Merely revealing the code does not ensure fairness; comprehensive auditing, through external regulators or internal reporting, helps identify and correct issues like discrimination or malfunction. This process is essential for AI systems with significant human impact.

Addressing these issues requires transparency, improved regulations, and responsible AI development practices. Bias in AI can be mitigated by diverse training datasets, while stricter policies can limit the misuse of generated content. Collaboration between tech companies, policymakers, and ethicists is crucial to ensure the responsible and ethical use of AI in society.

Cybersecurity is a type of technology that measures and prevents cyberattacks (an unauthorized action against computer infrastructure that compromises the confidentiality, integrity, or availability of its content) and mitigates their impact. In the relentless battle against cyber threats, innovation in cybersecurity is the key to staying ahead. Using machine learning (ML) and data analytics, the dynamic duo reshaping cybersecurity, systems can detect a fraudulent transaction in milliseconds, saving millions for businesses worldwide. ML enhances cybersecurity by detecting, analyzing, and responding to threats more efficiently, shifting from reactive to proactive measures,
ML impacts cybersecurity in key areas-

·       Detection of frauds: ML algorithms analyze vast datasets to identify patterns indicative of fraudulent activities, such as anomalous transactions or unauthorized access attempts, thus improving the response capabilities of the system.

Predictive Analytics for Risk Management: ML predicts future threats by analyzing data patterns, aiding proactive risk mitigation in predictive analytics for risk management.

Drowning stakeholders in a sea of numbers lifelessly stacked in boring tables is bound to either bore or overwhelm them. This also disconnects them from the key insight that you aim to present through the information in the first place. When stakeholders become overwhelmed with many detailed but plainly presented statistics, data points, or figures without appropriate context or visualization, they often find it difficult to comprehend the value of the information it represents for decision-making. This lack of interest, understanding, or action by employees then obstructs successful transmission and collaboration in the business.
Data visualization is an important part of data analysis that can transform the process of displaying relationships, patterns, and trends that were previously presented in a boring and monotonous graphs and tables. Visualization can help build compelling, concise, creative and extremely attractive infographics, charts, graphs and tables which can withhold the attention of any listener and help in communicating complex data easily and clearly. It may even help an analyst discover new patterns and relationships that may not have been apparent previously in the raw data. It breaks vast, complex data sets down to aid decision-making and offers up some nuggets of gold from the extensive, endless realm of data points. Some of top products that make use of this principle can be found below by category:
Data Heat Maps: Use color-coded data to optimize websites, akin to adjusting sunbeds for optimal exposure. Scatter Plots: Depict relationships between variables, revealing outliers and trends in ad spend versus revenue. Histograms: Group customer ages to showcase dominant age groups for targeted marketing. Bar Graphs: Compare market share among brands like Apple, Samsung, and Google, akin to a medieval data joust.
Data visualization is a crucial modern skill to possess in one’s arsenal. It can be performed by anyone at any stage with any type of data. Start your insight into data visualization today, learn more here and contact us!

At present, it is becoming less and less challenging to identify and target specific audience segments more effectively, optimize performance of social media ads and to create personalized content due to the emergence of Predictive Analytics. Predictive analytics, driven by Machine Learning (ML), allows digital marketers to predict future trends and user behavior, make smarter decisions and improve ad performance.

What is Predictive Analytics?

Predictive Analytics uses current or historical data and predicts plausible future trends, events and patterns. Such models have been in use for quite some time now, such as predicting the sales of tickets for a movie, understanding future staffing needs of a hospital or even forecasting a business’s financials at the end of an upcoming quarter. However, today this practice has evolved from simple manual predictive analysis to complex ML systems that are faster, way more effective and can be implemented on a much larger scale.
ML, a type of AI, uses algorithms to enhance prediction accuracy by analyzing data and making informed judgments. ML algorithms analyze datasets to find patterns and characteristics among users. In the context of social media marketing, this helps marketers to segment their audience accurately and effectively. It can also be used to customize ad content based on individual user preferences. Through analyzing data, it can predict which ads are likely to give the highest ROI.
According to the Crowdfire website, 57% of businesses that used machine learning to improve customer experience notice a 100% boost in customer loyalty, over 100% rise in brand awareness, 70% improvement in fraud detection and 28% increase in acquiring new customers. Therefore, using ML and AI, offers great benefits to a business in leading to higher growth, increasing loyalty and enhancing market position.
Every business wants to be a part of the AI movement, especially implementing in all business systems at the earliest, but do not do so as they have no idea where to begin. We can help you with that! Learn more and consult us today!

Large Language Models, better known as LLMs, are at the forefront of the ongoing Artificial Intelligence (AI) revolution that is transforming the world of technology. Popular representatives of AI such as OpenAI's ChatGPT and Google's Bard also deploy this astonishing technology, and the term "LLM" is mentioned constantly in discussions, events and keynotes. So, what exactly is an LLM? Let’s explore!

Large Language Models are a type of AI program, and to be more precise, a type of Machine Learning (ML) program. It is built on a neural network model known as transformer model. The model is fed large amounts of data, usually from well curated data sources and datasets found on the internet, and then trained to interpret diverse and complex types of data (including human language). Following this, Deep Learning (DL) is deployed to conduct an analysis of this unstructured data to distinguish between different pieces of input and research data. Through this process, LLMs are able to generate appropriate responses for any problem that they are presented with. 

LLM models are best used as a form of Generative AI (GenAI). GenAI can generate text-based responses to all kinds of problems and even write complex code in a matter of seconds! It also has several other applications such as sentiment analysis, customer service etc. As a technology it is still in its early stages, comprising of several key issues such as bugs and other types of manipulations. Regardless, LLMs are the next big thing in AI today, and are sure to become a staple of tomorrow.

If you belong to the world of Data, hackathon is not a new word to you. Several organizations host hackathons online but how do you pick the right one for yourself? Especially if you are a beginner?

What you do in a hackathon is only an easier version of what your job as a data scientist would require. From personal experience, Kaggle community is a boon to budding aspirants! It does wonders in enhancing one’s skillset by providing a competitive exposure.

The dataset on Kaggle and other platforms is created for the purpose of competitions and giving the participants a taste of work that data scientists are expected to do. However, real world data is much messier than what you would work with on these platforms. Nevertheless, it’s a great way to polish and upgrade your skills.

Read more at: https://analyticsindiamag.com/how-much-is-kaggle-relevant-for-real-life-data-science/

If you’re not tracking VR analytics, how do you know what works and what doesn’t? How do you prove ROI?

Utilizing quantitative and qualitative data can put you ahead of your competitors.  After all, without analyzing your data within VR is just guess work.

Doing this gives businesses the ability to track users in a 3D space instead of 2D screens. Traditional 2D tracking metrics such as clicks, swipes, scrolls or taps are certainly not the best ways to capture the depth of data available in these 3D environments.

VR specific metrics include Eye tracking to see what draws their attention, user interaction with specific objects, tracking 3D spatial data and biometrics to measure the emotional state of users to name a few.

These metrics are used by businesses to develop better products, train employees effectively and more efficiently and to understand customer buying behaviour.

Read more at: https://www.tobiipro.com/blog/why-vr-analytics-eye-tracking/

Each one of us have experienced Artificial Intelligence (AI) in our daily lives- from customized Netflix recommendations to personalized Spotify playlists to voice assistants like Alexa – all of these show how integral AI-enabled systems have become a part of our lives.

On the business front, most organizations are heavily investing in AI/ML capabilities. Whether it is automation of critical business processes, building an omni-channel supply chain or empowering customer-facing teams with chatbots, AI based systems significantly reduce manual work and costs for businesses leading to higher profitability.

However, Machine-learning systems are only as good as the data the are trained upon. Many AI experts believe that AI should be trained not only on simple worst-case scenarios but also on historical events like the Great Depression of 1930s, the 2007-08 financial crisis and the current COVID-19 pandemic.

Today, as humans rely on AI, they cannot leave AI to function by itself without human oversight because machines do not possess a moral or social compass. AI is as good as the data it is trained upon, which, may reflect the bias and though process of its creators.

Read more at: https://www.lionbridge.com/blog/3-reasons-why-ai-needs-humans/

No more will you hear about Chatbots being the next big thing. They’re already here and here to stay! Top domains where Chatbots are proving beneficial are:

1.      Ecommerce and Online Marketing: Messenger Chatbots have higher open rates and click through rates than Email, as a result of which many online marketers have begun using Chatbots as a way of getting website visitors’ information. Redirecting the customer to the correct sales channel, content gamification and relationship marketing are additional benefits it brings to this domain.

2.      Customer Service: The best use of technology right now is in automating the easy questions that get asked over and over again with a live agent takeover whenever the bot cannot answer a question. When the bot is stumped, it automatically sends the questions to a live agent, listens to the answer and then learn how to answer such questions in future.

3.      Travel, Tourism and Hospitality: Bots in this space are being successful on a number of critical fronts- they increase revenue, increase customer satisfaction, increase engagement and brand loyalty and lower costs via automation.

4.      Banking, Financial Services and Fintech: First and foremost, bots can help warn you about issues and dangers with your bank account. Bots can give you suggestions on what to do with your money- it can give you a cost breakdown of where you are spending or how can you move money around in order to save more money. Banks are also using chatbots internally to help automate tasks.

5.      HR and Recruiting: Chatbots can engage applicants and pre-screen them and make sure they’re qualified by asking a few questions. They also help in easing the process of on boarding new employees.

What other uses could be coming next? Read more at:

By definition, Data Science is a multi-disciplinary field that uses scientific methods, algorithms and systems to extract knowledge from structured and unstructured data. It processes enormous volume of information to draw meaningful conclusion and help businesses grow and expand.

Some of the biggest advantages analyzing data can give to your brand include improving efficiency, cut costs, boost sales, better recruitment, identifying opportunities and targeting the right set of audience to name a few.

Focusing on the practical ideas, four ways you can use big data to raise brand awareness include:

1.      Personalization: Analyzing consumer related information helps in understanding their preferences on an individual level. You can customize offers so as to fit each user individually.

2.      Choose the most relevant marketing channels: Brand awareness largely depends on marketing strategy and the channels you choose to promote business. For instance, Instagram marketing may help you attract younger users while marketing on LinkedIn gathers business professionals.

3.      Create better content: Data analytics enables you to learn about the buying persona such as education, relationship status, professional status, personal interests, demographics, leisure time activities, etc.

4.      Quality reporting: Using data science you can figure out the strengths and weaknesses of the brand, website traffic, social media performance and many other features.

Have you ever thought about incorporating data science into your business strategy?

Details at: https://www.business.com/articles/drive-business-growth-with-analytics/

Internet of Things is described as a digitally connected universe of everyday devices which are embedded with internet connectivity, sensors and other hardware which allow communication through web. From health tracking Fitbits to Smart blackboards, IoT has made everything around us smart. On a smaller scale, it would be switching on a TV using your phone and on a larger scale planning smart cities with sensors all over.

Why is IoT so important?

The sensors installed are capable of sending information and/or receiving information and acting upon it. These are beneficial as they help improve and innovate lives of customers, businesses and society at large. Businesses have invested extensively in R&D to innovate and develop out-of-the -box products.

Read more at:  https://www.zdnet.com/article/what-is-the-internet-of-things-everything-you-need-to-know-about-the-iot-right-now/

In literal sense, a forest is an area full of trees. Likewise, in technical sense, a Random Forest is essentially a collection of Decision Trees. Although both are classification algorithms which are supervised in nature, which one is better to use?

A Decision Tree is built on an entire data set, using all the features/variables while a Random forest randomly (as the name suggests) selects observations/rows and specific features/variables to build several decision trees and then average the results. Each tree “votes” or chooses the  class and the one receiving the most votes by majority is the “winner” or the predicted class.

A Decision tree is comparatively easier to interpret and visualize, works well on large datasets and can handle categorical as well as numerical data. However, choosing a comfortable algorithm for optimal choice at each node and decision trees are also vulnerable to over fitting.

Random Forests come to our rescue in such situations. Since they select samples and the results are aggregated and averaged, they are more robust than decision trees. Random Forests are a strong modelling technique than Decision Trees.

Read more at: https://www.analyticsvidhya.com/blog/2020/05/decision-tree-vs-random-forest-algorithm/

As the phrase goes, “everything that can be digitized, will be digitized”, is fast replaced by “If something can be run by algorithms, it will be”. Algorithms are supposed to be performing the following tasks: • Reading resumes: With natural language processing, resumes can be read faster and with more careful eyes. • Using spreadsheets: Soon the analysis made by experts using spreadsheets would be taken over by AI. • Hiring consultants: Since the analysis will all be done by algorithms, hiring consultants is really not needed as before. Hence, for coping up with the changes, one needs to get acquainted with the programs, rent a machine learning expert to design algorithms or make it on your own and invest for the future by learning new software. Read more at:https://www.experfy.com/blog/algorithms-are-replacing-leadership-strategies

NASA has used AI in human spaceflight, scientific analysis and autonomous systems. Multiple programs like CIMON, ExMC, ASE, Multi-temporal Anomaly Detection for SAR Earth Observations, FDL, robots and rovers are currently available at NASA. It is now working on overcoming the barriers that once blocked it from innovations in AI and Machine Learning. Although Machine Learning has been in existence for 60 years, benefits couldn’t be reaped by NASA because according to Brian Thomas, a NASA agency data scientist and program manager for Open Innovation, they are being held back. Read more at: https://www.aitrends.com/ai-world-government/how-nasa-wants-to-explore-ai/