Predicting Earthquakes

Researchers from Stanford's School of Earth, Energy & Environmental Sciences have developed a deep-learning model to detect seismic activity. The algorithms can identify the start of two different types of seismic waves and detect even weak earthquakes that current methods often overlook.

Scientists applied the model to five weeks of continuous earthquake data and located 200% more earthquakes than traditional technology found. If this type of AI software catches on, it could help people evacuate their homes before an earthquake occurs. It could also prevent people from returning home too early and encountering aftershocks.

Forecasting Floods

Climate change has caused a dramatic increase in flooding. In July 2022, the U.S. experienced two one-in-1,000-year rainfall events within two days of each other, leading to devastating floods that engulfed homes and claimed several lives. Although floods cause billions of dollars in damage annually and affect hundreds of millions of people, current forecasting technology often fails to help people evacuate in time.

Now, some researchers hope AI can help predict heavy rainfall. Google's AI-based Flood Hub software is available in 80 countries, warning people of floods up to a week in advance. Users can look at the world map to see rainfall and river level predictions for each region, with a red icon indicating the highest risk. Google is working on making the technology available in Search and Maps.

Detecting Wildfires

By the time firefighters extinguished the 2018 Camp Fire, it had claimed the lives of 85 people and burned for two weeks, making it the deadliest wildfire in California's history. Could AI have predicted the disaster and saved the towns of Paradise and Concow?

The California Department of Forestry and Fire Protection has started using high-tech cameras and AI to detect smoke and fire. A network of cameras mounted on platforms scan the horizon for wildfires, and researchers are training the software on what is and is not a fire.

One benefit of using cameras is that they can be where people cannot, such as in remote wilderness locations. Hopefully, this new technology will learn to alert firefighters when it detects a blaze and help prevent future disasters.

Predicting Hurricanes

NASA's IMPACT team recently partnered with tech company Development Seed to track Hurricane Harvey. Using machine learning and satellite imagery, the Deep Learning-based Hurricane Intensity Estimator estimates a hurricane's wind speed as soon as satellite data reaches Earth.

The software's neural networks essentially automate the Dvorak technique that matches satellite imagery to known patterns. By analyzing hurricane data in almost real time, meteorologists may be able to warn the public of impending hurricanes before disaster strikes.

Issuing Smarter Alerts

In addition to predicting disasters, AI could help by sending out timely alerts to save money, keep people informed and aid in the evacuation process.

For example, U.S. Coast Guard Command Centers have people listening for radio distress calls for 12 hours a day, which entails listening almost entirely to hoax calls or static. AI could relieve employees of this tedious duty by analyzing radio traffic to detect distress signals. This technology could help issue faster alerts to activate Coast Guard rescue missions.

Another potential use for AI would be to analyze CCTV footage in real time inside buildings, sounding an alarm if it detected smoke or earthquake-related tremors. A rapid response time would allow people to evacuate quickly.

Harnessing the Power of AI

Artificial intelligence is revolutionizing disaster forecasting. Meteorologists have already used it to evacuate people who would otherwise be in the direct path of oncoming storms, such as during India's Cyclone Phailin in 2013.

The technology will likely save countless lives as it becomes even more refined. Someday, instead of looking at the skies, we may only have to look at a screen to know when to board up the windows.

The post Why We Need AI to Keep People Safe from Natural Disasters appeared first on Datafloq.

Annotating data entails applying prepared labels or annotations to the data in accordance with the task at hand. During the training phase, the machine learning model draws on these annotations as the “ground truth” or “reference points.” Data annotation is important for supervised learning as it offers the necessary information for the model to generalize relationships and patterns within the data.

Data annotation in machine learning involves the process of labeling or tagging data with relevant information, which is used to train and improve the accuracy of machine learning algorithms. 

Different kinds of machine learning tasks need specific kinds of data annotations. Here are some important tasks to consider: 

Classification 

For tasks like text classification, sentiment analysis, or image classification, data annotators assign class labels to the data points. These labels indicate the class or category to which each data point belongs. 

Object Detection 

For tasks involving object detection in images or videos, annotators mark the boundaries and location of objects in the data along with assigning the necessary labels. 

Semantic Segmentation 

In this task, each pixel or region of an image is given a class label allowing the model to comprehend the semantic significance of the various regions of an image.

Sentiment Analysis 

In sentiment analysis, sentiment labels (positive, negative, neutral) are assigned by annotators to text data depending on the expressed sentiment.

Speech Recognition 

Annotators translate spoken words into text for speech recognition tasks, resulting in a dataset that combines audio with the appropriate text transcriptions.

Translation 

For carrying out machine translation tasks, annotators convert text from one language to another to provide parallel datasets.

Named Entity Recognition (NER) 

Annotators label particular items in a text corpus, such as names, dates, locations, etc., for tasks like NER in natural language processing.

Data annotation is generally performed by human annotators who follow particular instructions or guidelines provided by subject-matter experts. To guarantee that the annotations appropriately represent the desired information, quality control, and consistency are crucial. The need for correct labeling sometimes necessitates domain-specific expertise as models get more complex and specialized.

Data annotation is a crucial stage in the machine learning pipeline since the dependability and performance of the trained models are directly impacted by the quality and correctness of the annotations.

Significance of Quality Data Annotation for Machine Learning Models

In order to comprehend how quality data annotation affects machine learning model performance, it is important to consider several important elements. Let's consider those: 

Training Data Quality 

The quality of training data is directly impacted by the quality annotations. Annotations of high quality give precise and consistent labels, lowering noise and ambiguity in the dataset. Annotations that are not accurate can lead to model misinterpretation and inadequate generalization to real-world settings.

Bias Reduction

An accurate data annotation assists in locating and reducing biases in the dataset. Biased models may produce unfair or discriminatory predictions as a result of biased annotations. Before training the model, researchers can identify and correct such biases with the help of high-quality data annotation.

Model Generalization

A model is better able to extract meaningful patterns and correlations from the data when the dataset is appropriately annotated using data annotation services. By assisting the model in generalizing these patterns to previously unexplored data, high-quality annotations enhance the model's capacity to generate precise predictions about new samples.

Decreased Annotation Noise

Annotation noise i.e. inconsistencies or mistakes in labeling is diminished by high-quality annotations. Annotation noise might be confusing to the model and have an impact on how it learns. The performance of the model can be improved by maintaining annotation consistency.

Improved Algorithm Development

For machine learning algorithms to work successfully, large amounts of data are frequently needed. By utilizing the rich information present in precisely annotated data, quality annotations allow algorithm developers to design more effective and efficient models.

Efficiency of Resources

By decreasing the need for model training or reannotation owing to inconsistent or incorrect models, quality annotations help save resources. This results in faster model development and deployment. 

Domain-Specific Knowledge

Accurate annotation occasionally calls for domain-specific knowledge. Better model performance in specialized areas can be attained by using high-quality annotations to make sure that this knowledge is accurately recorded in the dataset.

Transparency and Comprehensibility

The decisions made by the model are transparent and easier to understand when annotations are accurate. This is particularly significant for applications, such as those in healthcare and finance, where comprehending the logic behind a forecast is essential.

Learning and Fine-Tuning

High-quality annotations allow pre-trained models to be fine-tuned on domain-specific data. By doing this, the model performs better on tasks related to the annotated data.

Human-in-the-Loop Systems

Quality annotations are crucial in active learning or human-in-the-loop systems where models iteratively request annotations for uncertain cases. Inaccurate annotations can produce biased feedback loops and impede the model's ability to learn.

Benchmarking and Research

Annotated datasets of high quality can serve as benchmarks for assessing and comparing various machine-learning models. This quickens the pace of research and contributes to the development of cutting-edge capabilities across numerous sectors.

Bottom Line

The foundation of a good machine learning model is high-quality data annotation. The training, generalization, bias reduction, and overall performance of a model are directly influenced by accurate, dependable, and unbiased annotations. For the purpose of developing efficient and trustworthy machine learning systems, it is essential to put time and effort into acquiring high-quality annotations.

The post The Impact of Quality Data Annotation on Machine Learning Model Performance appeared first on Datafloq.

Humanity stands at the brink of a new chapter – one where the realms of biology and technology coalesce to unlock extraordinary frontiers of knowledge and innovation. We are crossing into the era of Biohacking 2.0, underpinned by monumental scientific breakthroughs that have illuminated the intricate molecular fabric of life. Equipped with these insights and tools that enable precise manipulation, we are positioned to reimagine and augment our collective potential responsibly and ethically.

Decoding the human genome has provided profound clarity into the fundamental code that orchestrates the symphony of life. Meanwhile, revolutionary tools like CRISPR enable biohackers to edit genes with remarkable precision, allowing exploration of reshaping DNA sequences in creative ways. The rise of AI systems like DeepMind's AlphaFold has enabled accurate prediction of protein structures that long evaded scientists. This computational prowess empowers biohackers to understand and re-engineer biological molecules and pathways.

These scientific leaps are creating ripples far beyond academia, disrupting traditional industries and business models. Fields spanning pharmaceuticals, agriculture, materials and manufacturing face dramatic shifts as biotechnology enables hyper-personalized treatments, enhances human capabilities, and spawns disruptive innovations. A new generation of implantable devices and interfaces will emerge. Consumer marketing is also evolving to provide intensely personalized plans based on biomarker data.

Amidst the excitement of transformation, responsible progress remains the guiding light. As tools emerge to enhance innate human capacities and sculpt biology like never before, critical ethical frontiers regarding access, unintended harms, and hubris come into sharp focus. Moving forward, anchoring innovation in wisdom and foresight will be vital. Biotechnology's immense power must uplift our species as a whole, not fracture it. International and interdisciplinary dialogue to shape constructive policies will be key.The era of Biohacking 2.0 is dawning, marked by the convergence of biology and technology to propel humanity into an extraordinary future underpinned by responsible progress.

At this historic inflection point, the dawn of Biohacking 2.0 beckons us to a luminous future. One where the mysteries of life become open books for us to gently reshape in service of humanity's ascent. Where human imagination and ethics shine in unison, guiding us across new frontiers. As the arcs of exploration and responsibility intersect, a tomorrow awaits where our collective ingenuity elevates both our capabilities and spirit to unprecedented heights. The odyssey has just begun.

To read the full article, please go to TheDigitalSpeaker.com

Images: Midjourney

The post The Biohacking Odyssey: Where Biology and Technology Converge to Reimagine Our Future appeared first on Datafloq.

Understanding the common causes of data leaks is crucial for implementing effective cybersecurity measures. In this article, we explore five of the prominent causes, highlighting how they occur as well as examples to demonstrate the pervasiveness of data leaks.

Software Misconfiguration

Despite the apparent danger, many individuals and organizations leave default passwords unchanged. This is just one example of how misconfiguration of settings can allow attackers to infiltrate systems, databases, cloud services, applications, etc. At other times, a misconfiguration can occur when a program's settings do not align with the organization's security policy, and so permits unintended behavior.

This is basic cyber hygiene, but even big tech companies can leave certain things out. Back in 2021, for example, Microsoft made the news for the wrong reasons when 38 million customer records including sensitive information were exposed online due to a misconfiguration of its Power Apps portal service.

Particularly, organizations should be careful when migrating services or data to cloud environments – misconfigurations are common with this action and can arise simply from not following or not understanding the instructions.

Ransomware

According to a recent report on the state of ransomware, global ransomware attacks surged in the past year and recorded an all-time eye. Amidst these, the US is the biggest victim, suffering 43% of global recorded attacks, with zero-day exploitations by malicious actors playing a huge role in the increase in attacks.

So, ransomware attacks are not only growing in number but also in sophistication. And for this, organizations have to heighten their vigilance to prevent data leaks.

Source: Statista

Notably, DISH, the satellite broadcast company was hit by a ransomware attack in February. The attack led to significant outages in its internal servers and IT systems and leaked personal information belonging to about 300,000 people. But this is only one of the several ransomware attacks that have hit various organizations and facilities.

Data Theft

Over the past few years, insider attacks have become a growing concern, with malicious insiders becoming particularly a worry for data theft. Such concerns contributed to the development of zero-trust cybersecurity solutions since anyone can be a malicious insider, with greater risks assigned to privileged users with access to sensitive information.

Source: Statista

This is not to rule out the role of external elements in data theft, though. A German newspaper, earlier this year, ran a report detailing a myriad of safety concerns expressed by Tesla customers. According to the electric car company, the confidential data provided to the newspaper was stolen from its system, although it couldn't tell whether an insider was responsible or an external actor.

Third-Party Breach

Third-party breaches have become a particularly beneficial mode of attack for malicious actors because of the potential of acquiring several victims from just one hit. For instance, according to a third-party breach report, in 2022, 63 vendor attacks led to 298 data breaches across companies.

In January, two insurance companies – Aflac and Zurich Auto Insurance – suffered a data leak that affected millions of records including the information of at least 2 million policyholders with the two companies combined. According to reports, an unnamed US subcontractor was involved although it was not certain that both data breaches were connected.

This shows the cascading effects of third-party data breaches and underscores why organizations must stop at nothing to ensure that they only partner with companies and vendors that have strong security protocols in place.

Software (API) Vulnerability

APIs were a groundbreaking revelation in software development, but their proliferation has exacerbated the risks of data exposure since sensitive data is increasingly being shared via this medium. So, API vulnerabilities, such as broken authentication issues easily jeopardize the software's security and can allow malicious actors to access data illegally.

Source: VentureBeat

An API vulnerability in Twitter's software allowed threat actors to steal the email records of over 200 million users. Although this happened back in 2021 and the breach was fixed in January the following year, by mid-2022, the data sets started going on sale on the dark web and were eventually published for free. Email data are typical targets for phishing and social engineering attacks.

How to Prevent Data Leakage

Preventing data leakage is not an impossible task, although, due to the increasingly sophisticated nature of cyber attacks these days, it can be very tough to handle. However, these few steps should help you overcome the most common causes of data leakage.

1. Implement a strong data detection and response solution: Unlike traditional data loss prevention systems, DDR solutions prioritize behavioral analytics and real-time monitoring via machine learning to automatically identify and respond to data incidents.
2. Evaluate third-party risks: working with a third party, especially when it involves exchanging data, can no longer be business as usual. The risks of your partners are yours too, so you must know where both companies stand and how you can complement, not endanger, each other, security-wise.
3. Secure all endpoints: there has been a huge increase in the number of remote access points that communicate with business networks. Plus, they are dispersed too, and sometimes internationally. Adopting a zero-trust approach helps prevent endpoints from becoming leeway for attacks.
4. Cybersecurity hygiene: as identified earlier, data leakage can simply be due to unhygienic practices. Methods such as encryption, data backups, password management, etc. are not outdated; they should all be in place to help you maintain your guard.

Conclusion

Proactive measures, regular security assessments, and a comprehensive cybersecurity strategy are key to mitigating the risks associated with data leakage. As we have seen from the examples, every kind of business, even the biggest tech companies, suffers from this challenge. Therefore, data security is something that all business leaders must take seriously from now.

The post Most Common Causes of Data Leakage in 2023 appeared first on Datafloq.

Every hotel, whether a hotel chain or a boutique inn, wants to provide the best experience while maximizing profits.

But how?

Enter Hotel Data Intelligence, the compass that savvy hoteliers are using to navigate the competitive seas of the industry.

By the end of this piece, you'll be equipped with insights that could very well be your hotel's next game-changer.

What is Hotel Data Intelligence?

Simply put, hotel data intelligence is the superhero behind the curtains, bringing together all tidbits of data about hotels and their guests, and turning them into powerful, actionable insights.

We're talking about booking patterns, those guest reviews you obsess over, competitor pricing, and even broad market conditions.

And here's the kicker – it's the goldmine that many hotels are overlooking.

Gaining that Elusive Competitive Edge

Alright, buckle up!

Let's chat about the magic that hotel data intelligence can sprinkle over your hotel business:

1. Strategic Pricing

Remember the last time you fretted about room rates?

With hotel data intelligence, those days are behind you.

By analyzing past booking trends, you can predict when the demand will peak, or when you might hear crickets.

And armed with this knowledge?

You can set room rates that not only appeal to guests but also keep your revenue game strong.

But wait, aren't you forgetting something?
Probably competitive pricing!
In the bustling hotel industry, competitive pricing stands out as one of the most crucial elements

For getting your competitor's data you can use a hotel price API.
It will help you to constantly keep an eye on your competitors which will help you analyze where your rate stands, whether it is competitive enough or wrong enough to repel the customers.

With such a tool, you're not just setting prices, you're setting the right prices.

2. Reputation Management

Let's get real; we all love compliments, don't we?

But in the world of hospitality, compliments and criticisms are equally precious.

Hotel data intelligence acts like your personal reputation manager, sieving through guest reviews and online chatter about your establishment.

And the best part? You can spot the patterns.

Multiple guests pointed out that the breakfast spread could be better. Bingo! There's your cue to revamp the morning menu.

The power to pivot based on real feedback? That's invaluable.

3. Marketing

In a world bursting with ads, standing out is crucial. And hotel data intelligence can hand you the microphone.

By diving deep into data, you can know who your guests are, what they love, and even what they might want in the future. And then? You tailor-make campaigns that resonate, engage, and convert.

For example, if the data tells you that most of your guests are nature lovers, then how about a weekend package that includes a nature walk?

Or if your guests are predominantly business travelers, why not throw in a mid-week relaxation package with spa discounts?

The possibilities are endless, and the data points the way!

Concluding:

So, the next time someone mentions Hotel Data Intelligence, I hope you're filled with possibilities and not puzzled looks.

It's like having a magical crystal ball that doesn't just predict the future but helps you shape it.

So, what's the next step?

Get out there, embrace the power of data, and watch as your hotel not only competes but shines in this ever-evolving market.

And remember, in the age of information, it's not the biggest, but the smartest players that come out on top.

Embrace hotel data intelligence, and be that smart player!

The post How to Use Hotel Data Intelligence to Gain Competitive Edge? appeared first on Datafloq.

The advent of advanced algorithms, Big Data, and the exponential increase in computing power has propelled AI‘s transition from theory to real-world apps.

However, AI has also unveiled a darker side, attracting cyber attackers to weaponize the technology and create havoc in ways unimaginable!

Deloitte states that 34.5% of organizations experienced targeted attacks on their accounting and financial data in 12 months. This shines a light on the importance of maintaining a risk register for tracking potential threats.

Another research further emphasizes this – a staggering 80% of cybersecurity decision-makers acknowledge the need for advanced cybersecurity defenses to combat offensive AI. Let us dive deep into the double-edged nature of the technology.

Top 4 AI-enabled phishing and cybersecurity threats to know

Cyber threats are on the rise, both in terms of complexity and volume. Here are four examples that are creating a buzz in today's security landscape for all the wrong reasons:

1. Deepfakes

This manipulative technique creates realistic-looking and highly convincing video, audio, and image content that impersonates individuals and organizations using AI algorithms.

Deepfakes can push fake news or negative propaganda to confuse or skew public opinion and imitate the victim's voice or appearance to gain unauthorized access to secure systems.

Using this technology, cyber attackers can instruct employees to perform actions that compromise the organization's security, such as sharing confidential data or transferring funds.

Remember when in 2019, the CEO of a UK-based energy firm got scammed into wiring 220,000 to a scammer's bank account because he thought he was speaking to his boss on the phone, who had the recognizable “subtle German accent?”

The voice, in fact, belonged to a fraudster who used AI voice technology to spoof the German chief executive. Deepfakes are known to make phishing attempts much more personable and believable!

2. Data poisoning

While data poisoning is typically associated with Machine Learning (ML), it can also be applied in the context of phishing.

It is a type of attack where misleading or incorrect information is intentionally inserted into a dataset to maneuver the dataset and minimize the accuracy of a model or system.

For example, most people know how prominent social media companies like Meta and Snap handle data. Yet, they willingly share personal info and photos on the platforms.

A data poisoning attack can be launched on these platforms by slowly corrupting data integrity within a system. Once the data gets tainted, it leads to several negative consequences, such as:

• Inaccurate predictions or assumptions
• Disruptions in day-to-day operations
• Manipulation of public opinion
• Biased decision-making

Ultimately, data poisoning is considered a catalyst for financial fraud, reputation damage, and identity threat.

3. Social engineering

It typically involves some form of psychological manipulation, fooling otherwise unsuspecting individuals into handing over confidential or sensitive information that may be used for fraudulent purposes.

Phishing is the most common type of social engineering attack. By leveraging ML algorithms, cyber attackers analyze volumes of data and craft convincing messages that bypass conventional cyber security measures.

These messages may appear to come from trusted sources, such as reputable organizations and banks. For example, you might have come across an SMS or email like:

• Congrats! You have a $500 Walmart gift card. Go to “http://bit.ly/45678” to claim it now.
• Your account has been temporarily locked. Please log in at “http://goo.gl/45678” to secure your account asap!
• Netflix is sending you a refund of $56.78. Please reply with your bank account and routing number to receive your money.

Cyber attackers want to evoke emotions like curiosity, urgency, or fear in such scenarios. They hope you would act impulsively without considering the risks, potentially leading to unauthorized access to critical data.

4. Malware-driven generative AI

The powerful capabilities of ChatGPT are now being used against enterprise systems, with the AI chatbot generating URLs, references, functions, and code libraries that do not exist.

Through this, cyber attackers can request a package to solve a specific coding problem only to receive multiple recommendations from the tool that may not even be published in legitimate repositories.

Replacing such non-existent packages with malicious ones could deceive future ChatGPT users into using faulty recommendations and downloading malware onto their systems.

How to protect your organization against AI phishing scams

As the sophistication levels of cyber attacks continue to evolve, it is essential to adopt several security measures to keep hackers at bay, including:

1. Implement the Multi-Factor Authentication (MFA) protocol

As the name suggests, MFA is a multi-step account login process that requires additional info input than just a password. For instance, users might be asked to enter the code sent on their mobile, scan a fingerprint, or answer a secret question along with the password.

MFA adds an extra layer of security and reduces the chances of unauthorized access if credentials get compromised in a phishing attack.

2. Deploy advanced threat detection systems

These systems use ML algorithms to analyze patterns, identify anomalies, and proactively notify users about potentially dangerous behaviors such as deepfakes or adversarial activities, thereby giving organizations a leg up over cybercriminals and other threat actors.

Many Security Operational Centers use Security Information and Event Management (SIEM) technology in tandem with AI and ML capabilities to enhance threat detection and notification.

The arrangement allows the IT teams to focus more on taking strategic actions than firefighting; it improves efficiency and cuts down the threat response time.

3. Establish Zero Trust architectures

Unlike traditional network security protocols focusing on keeping cyber attacks outside the network, Zero Trust has a different agenda. Instead, it follows strict ID verification guidelines for every user and device attempting to access organizational data.

It ensures that whenever a network gets compromised, it challenges all users and devices to prove that they are not the ones behind it. Zero Trust also limits access from inside a network.

For instance, if a cyber attacker has gained entry into a user's account, they cannot move within the network's apps. In a nutshell, embracing Zero Trust architectures and integrating them with a risk management register helps create a more secure environment.

4. Regularly update security software

This measure is commonly overlooked, and it is essential for maintaining a strong defense against AI-driven phishing and cyber security threats. Software updates include patches that address known anomalies and vulnerabilities, ensuring your systems are safe and secure.

5. Educate and train your employees

Training programs come in handy to raise awareness about the tactics employed by cyber attackers. You must, therefore, have the budget for teaching your employees different ways to identify various phishing attempts and best practices for responding to them.

Over to you

The role of AI in phishing indeed represents a frightening challenge in this day and age. Addressing such cybersecurity threats requires a multi-faceted approach, including user education, advanced detection systems, awareness programs, and responsible data usage practices.

Employing a systematic risk register project management approach can help you enhance your chances of safeguarding sensitive data and brand reputation. In addition, you should work closely with security vendors, industry groups, and government agencies to stay abreast of the latest threats and their remediation.

The post Digital Deception: Combating The New Wave Of AI-Enabled Phishing And Cyber Threats appeared first on Datafloq.

A Commonly Discussed Generative AI Use Case

Text based chatbots have been around for a long time and are now ubiquitous on corporate websites. Companies today are now scrambling to use ChatGPT or similar toolsets to upgrade their website chatbots. There is also lots of talk about voice bots handling calls by reciting the text generated in answer to a customer's question. This sounds terrific, and it is hard not to get excited at first glance about the potential of such an approach. The approach has a major flaw, however, that will derail efforts to implement it.

Let's first look at the common misunderstanding that makes such use cases inappropriate and then we can discuss a better, more realistic solution.

Same Question, Different Answers!

I've written in the past about how all generative AI responses are effectively hallucinations. When it comes to text, generative AI tools literally generate answers word by word using probabilities. People are now widely aware that you can't take an answer from ChatGPT as true without some validation. What most people don't yet realize is that, due to how it is configured, you can get totally different answers to the exact same question!

In the image below, I asked ChatGPT to “Tell me the history of the world in 50 words”. You can see that while there are some similarities, the two answers are not nearly the same. In fact, they each have some content not mentioned in the other. Keep in mind that I submitted the second prompt literally as soon as I got my first answer. The total time between prompts was maybe 5 seconds. You may be wondering, “How can that be!?” There is a very good and intentional reason for this inconsistency.

Injecting Randomness Into Responses

While ChatGPT generates an answer probabilistically, it does not literally pick the most probable answer. Testing showed that if you let a generative language application always pick the highest probability words, answers will sound less human and be less robust. However, if you were to force only the highest probability words you would, in fact, get exactly the same answer every time for a given prompt.

It was found that choosing from among a pool of the highest probability next words will lead to much better answers. There is a setting in ChatGPT (and competing tools) that specifies how much randomness will be injected into answers. The more you desire a factual answer to a question, the less randomness is desired because the best answer is preferred. The more creativity desired, such as creating a poem, the more randomness should be allowed so that answers can drift in unexpected ways.

The key point, however, is that injecting this randomness takes what are already effectively hallucinated answers and makes them different every time. In most business settings, it isn't acceptable to have an answer generated each time a given question is asked that is both different and potentially flawed!

Forget Those Generative AI Chatbots

Now let's tie this all together. Let's say I'm a hotel company and I want a chatbot to help customers with common questions. These might include questions about room availability, cancellation policy, property features, etc. Using generative AI to answer customer questions means that every customer can get a different answer. Worse, there is no guarantee that the answers are correct. When someone asks about a cancellation policy, I want to provide the verbatim policy itself and not generate a probabilistic answer. Similarly, I want to provide actual room availability and rates, not probabilistic guesses.

The same issue arises when asking for a legal document. If I need legal language to address ownership of intellectual property (IP), I want real, validated language word for word since even a single word change in a legal document can have big consequences. Using generated language for IP protection as-is with no expert review is incredibly risky. The generated legalese may sound great and may be mostly accurate, but any inaccuracies can have a very high cost.

Use An Ensemble Approach To Succeed

Luckily, there are approaches already available that will avoid the issues with the inaccuracy and inconsistency of generative AI‘s text responses. I wrote recently about the concept of using ensemble approaches and this is a case where an ensemble approach makes sense. For our chatbot, we can use traditional language models to diagnose what question a customer is asking and then use traditional searches and scripts to provide accurate, consistent answers.

For example, if I ask about room availability, the system should check the actual availability and then respond with the exact data. There is no information that should be generated. If I ask about a cancellation policy, the policy should be found and then provided verbatim to the customer. Less precise questions such as “what are the most popular features of this property” can be mapped to prepared answers and delivered much in the way a call center agent uses a set of scripted answers for common questions.

In our hotel example, generative AI isn't needed or appropriate for the purpose of helping customers answer their questions. However, other types of models that analyze and classify text do apply. Combined with repositories that can be accessed once a question is understood to find the answer will ensure consistent and accurate information is provided to customers. This approach may not be using generative AI, but it is a powerful and valuable solution for a business. As always, don't focus on “implementing generative AI” but instead focus on what is needed to best solve your problem.

Originally posted in the Analytics Matters newsletter on LinkedIn

The post No, That Is Not A Good Use Case For Generative AI! appeared first on Datafloq.

Ethical data mining seeks to gather and use information to help consumers while protecting their privacy as much as possible. That typically involves collecting less data, obfuscating it, being transparent about collection policies and requiring user consent. It's an important step forward in analytics but a challenging one.

Here are five significant obstacles to ethical data mining we must overcome.

1. Convenience vs. Privacy

The biggest issue in ethical data mining is the battle between privacy and effective analytics. Artificial intelligence (AI) and other technologies typically work better with larger data sets, but that means potentially putting more information at risk. Consequently, businesses often face a choice between making a service convenient and respecting users' privacy.

Personalized health care is a prime example of this issue. Medical organizations can offer more personal services, ensuring better patient outcomes, if they gather more data on patients to understand their unique situations. However, health care data breaches are becoming larger and more common as information technologies in the sector grow.

If collecting more data would mean better services for the customer but a possible breach of privacy, which path do companies choose? Which is better for the end user? Striking a balance between these two sides is far from easy.

How to Overcome It

Balancing these seemingly contradictory sides begins with understanding what data an organization actually needs. Hospitals may require patients' medical history to offer personalized care, but they don't need to store names, addresses, web browsing behavior or financial information.

Consequently, they can protect patients' privacy by only collecting the data they need and replacing identifiers like names with other identifiers. That way, they can keep track of records within the hospital, but the information would be meaningless to an outsider. Other organizations can follow similar practices. Only collecting essential data and obfuscating personally identifiable information (PII) will ensure privacy while enabling effective analytics.

2. Legality vs. User Expectations

Another dynamic complicating ethical data mining is what's legal and what users think is fair. Some companies may think their information collection policies are moral because they meet regulatory guidelines, but their customers could think otherwise.

TikTok asks for users' permission to collect and use their data, but it asks for more than it needs, according to some inside sources. Consequently, while its practices may be legally safe because they have user consent, some people may feel the company has misled them. That clash can create a public backlash and reduce consumer trust.

Laws like Europe's General Data Protection Regulation (GDPR) and the California Consumer Privacy Act (CCPA) provide a baseline for privacy in data mining but aren't complete. Many use language like “sufficient protections” and “reasonable privacy,” but those are highly subjective terms, so they don't offer much guidance. Businesses that use these as their sole measure of ethical data mining may still unintentionally breach users' trust.

How to Overcome It

The first step to addressing this ethical data mining challenge is to be upfront about what a company collects and why. Businesses shouldn't hide this information behind long blocks of text in a user agreement, either. Apps can give users a brief overview of what information is gathered and why, linking to a page with in-depth explanations if users want to know more.

Organizations should also pay attention to user opinions. Businesses must watch for what similar companies face backlash over and survey customers about what kinds of data mining they believe are reasonable and fair.

More than half of all consumers are willing to exchange data with businesses as long as there's a clear benefit, but 77% say transparency around how it's used is important to that decision. Involving them in the process by collecting feedback will help establish more trust.

3. Third-Party Risks

Third-party practices also pose a challenge to ethical data mining. A company may be fully transparent in its own data practices but pass information to a less secure or moral third party. Businesses have little control over their partners' policies, so ensuring an entire data ecosystem meets these standards can be difficult.

Take marketing, one of the biggest uses of data mining, for example. An agency may only analyze the minimum information necessary to create relevant ads, ask users for their consent, obfuscate data as much as possible and meet all regulatory guidelines. However, if the social media platforms it gathers this information from or any other tools it uses don't adhere to similar principles, it may contribute to privacy breaches.

Data mining practices must consider all involved parties to be truly ethical. More than half of organizations are experiencing a data breach from a third-party vulnerability, so these concerns are more prevalent than ever.

How to Overcome It

Rising regulations will help provide a minimum standard for fair data usage. Only five states have comprehensive data privacy laws, but 39 have considered them since 2018. As this legislation grows, it'll hold more businesses to a higher standard, establishing more trust in third parties. However, companies must also remember to go beyond and consider consumer expectations, not just the letter of the law.

Companies should also inform customers about any data-sharing with other parties, as 70% of consumers today say sharing information with other vendors without consent is unacceptable.

Higher security measures will also help minimize these risks. Implementing the principle of least privilege, which only allows each party, device or app access to what it needs to do its job, will ensure third parties can't access too much. Consequently, third-party breaches will be less likely and less impactful.

4. Transparency

Similarly, data mining practices need more transparency to become ethical. The first step to this goal is being upfront about what information a company collects and what it uses it for. However, many organizations lack visibility in their internal processes, making these permission requests misleading.

As many as 54% of IT decision-makers don't know where they store all their sensitive data. Many businesses don't use everything they collect, making it easier to misplace or overlook some information. Organizations that lack this insight can't reasonably secure users' data or be fully transparent about how they manage it, hindering trust with consumers.

A business can only be upfront about what it's aware of. Consequently, visibility must improve for data mining operations to achieve the level of trust and openness they need to be ethical.

How to Overcome It

Automation can provide the insight many organizations lack. Automated data discovery tools can scan companies' networks to find potential security risks and reveal what information the business really uses and how. Once they have that information, organizations can stop collecting what they don't use, apply necessary security fixes and inform users about their data mining policies.

Similarly, companies should use data mapping tools to understand how their systems use each piece of information. Creating, updating and auditing these maps will keep businesses current in their data practices, giving them the transparency they need to explain more to customers.

5. Unclear Governance Roles

A lack of clarity over information governance roles and responsibilities also holds ethical data mining back. An organization may have rules about appropriate storage and usage, but it must also have clear enforcement mechanisms and outlined roles for them to be useful.

Many data governance structures leave too much room for human error, which accounts for 88% of all breaches, according to some experts. A company can't reasonably expect workers to adhere to best practices if it's unclear what every employee should do to protect sensitive data. Similarly, unsafe and malicious practices can quickly slip between the cracks without a formal process for enforcing policies.

How to Overcome It

It's easy to miss the organizational side of ethical data mining, but technical defenses alone are insufficient. Businesses must outline formal, clearly communicated roles and responsibilities to maintain high standards.

Similarly, companies must create a detailed enforcement policy. That could look like regular audits to review how each team and employee adheres to data governance policies and specific actions to take for each infraction type. These actions can span from temporary loss of privileges for small or first-time offenses to termination for more extreme cases. Communicating these consequences with employees will encourage more compliance with these guidelines.

The Way Forward

These challenges are concerning, but they don't mean ethical data mining is impossible. Rather, they highlight where and how organizations must improve to balance analysis and privacy.

Finally, businesses must recognize that ensuring ethical data mining will take a cultural change. Practices and policies should focus on what's best for the end user at all times, so teams should ask themselves how each decision impacts them at each step in development. Regularly surveying customers about data collection, personalized services and related issues will clarify these choices' impacts.

The very organizational structure of the company should ensure accountability and transparency. Instead of adding security measures and data discovery after implementing a new service, companies should review their cybersecurity and privacy measures throughout the development cycle. It'll be easier to meet rising standards as companies focus on providing privacy and visibility from the beginning.

Ethical Data Mining Is Challenging but Crucial

Ethical data mining may seem like an oxymoron to some, but it's possible. Organizations that recognize these challenges can work to overcome them. As they do that, they'll create a safer, more comfortable online environment for their users.

Ethical data mining becomes increasingly important as businesses rely more on data and cybercrime grows. Achieving that is a challenging but essential goal.

The post Top 5 Challenges in Ethical Data Mining We Need to Overcome appeared first on Datafloq.

One of the most significant advantages of human-AI collaboration is the ability to leverage the strengths of both humans and machines. Humans bring creativity, empathy, intuition, and critical thinking skills to the table, while AI provides speed, accuracy, scalability, and deep data analysis capabilities. Through collaborative efforts in cloud environments, humans can focus on complex problem-solving tasks requiring higher-level cognitive abilities while offloading routine and time-consuming tasks to AI systems. This allows workers to optimize their time and energy resources towards more meaningful work that requires human judgment and expertise.

The successful implementation of human-AI collaboration in cloud environments requires careful planning and design. Employers must ensure that workers have adequate training to effectively interact with AI systems. Additionally, effective communication channels between humans and machines need to be established to enable seamless collaboration. As organizations continue to embrace this transformative approach to work processes in cloud environments, we can expect an era where machines augment our capabilities rather than replace them completely.

Understanding Cloud Environments: A Brief Overview

Cloud environments have revolutionized the way businesses operate by providing easily accessible and scalable infrastructure resources. At its core, a cloud environment is a virtualized space that allows users to access various software applications and storage capacities through the internet. This means that employees can collaborate from anywhere with an internet connection, eliminating the need for physical proximity.

Understanding the different types of cloud environments is crucial for organizations looking to harness their potential. Public clouds are owned and maintained by third-party service providers, offering services to multiple clients via the internet. On the other hand, private clouds are dedicated to a single organization, providing enhanced security and control over data. Hybrid clouds combine both public and private elements, allowing organizations to leverage the benefits of each approach while balancing cost-effectiveness and security.

Redefining Workflows with Human-AI Collaboration

In the fast-paced and ever-evolving digital landscape, businesses are increasingly seeking ways to enhance productivity and efficiency. One emerging trend that holds great potential is the collaboration between humans and artificial intelligence (AI) in workflows. Traditionally, workflows have been designed around the capabilities of human workers alone, but integrating AI into these processes opens up a world of possibilities.

Human-AI collaboration redefines workflows by leveraging the unique strengths of both humans and machines. While humans excel at creativity, critical thinking, and complex decision-making, AI brings unparalleled speed, accuracy, and ability to process massive amounts of data. By combining these attributes in workflow design, organizations can achieve higher levels of efficiency while benefiting from human insight and adaptability.

The utilization of AI-driven customer service revolutionizes workflows, enabling businesses to scale and adapt with utmost ease. As AI systems continue to evolve and excel in comprehending natural language processing (NLP) or natural language generation (NLG), they take over mundane tasks formerly handled by human agents. This liberates employees from repetitive responsibilities, empowering them to concentrate on more significant endeavors that necessitate their expertise and proficiency.

However, it is crucial to note that human-AI collaboration should not be seen as a replacement for human workers but rather as a tool to augment their abilities. Humans possess empathy, emotional intelligence, context comprehension – qualities that are essential in many business contexts where decision-making involves unpredictable variables or ethical considerations.

Benefits of Human-AI Collaboration in Cloud Environments

One of the key benefits of human-AI collaboration in cloud environments is an enhanced decision-making process. By leveraging AI capabilities, humans can access vast amounts of data and gather valuable insights to aid in decision making. This partnership allows for faster and more accurate decisions, as AI algorithms can quickly analyze complex data sets and provide recommendations based on patterns and trends that may not be immediately apparent to humans.

Advantage of human-AI collaboration in cloud environments is the ability to automate mundane tasks, freeing up time for more strategic and creative work. AI technologies can handle repetitive tasks such as data entry or document analysis, allowing humans to focus on higher-level thinking, problem-solving, and innovation. This shift in workload distribution leads to increased productivity and efficiency within organizations.

Moreover, human-AI collaboration enables continuous learning and improvement over time. As humans work alongside AI systems in cloud environments, they can provide feedback and fine-tune algorithms for better performance. The combination of human intuition with machine learning capabilities allows for iterative improvements that enhance the accuracy of predictions, optimize workflows, and drive innovation.

Overall, human-AI collaboration in cloud environments offers a transformative approach to work processes. By harnessing the strengths of both humans and AI systems, organizations can unlock new possibilities for smarter decision-making, increased productivity, and continuous improvement. Embracing this collaborative model not only leads to tangible benefits but also paves the way for innovative advancements that reshape industries across various sectors.

Challenges and Limitations of Human-AI Collaboration

One of the major challenges of human-AI collaboration is the lack of trust. Humans tend to be skeptical of AI systems, fearing that they will replace their jobs or make errors that could have serious consequences. As a result, they may be hesitant to fully rely on AI recommendations or decision-making capabilities. Building trust between humans and AI systems requires transparency and clear communication about how the technology works and its limitations.

Another limitation of human-AI collaboration is the potential bias in AI algorithms. Machine learning models are trained on vast amounts of data, which can inadvertently reflect biases present in society. These biases can then be perpetuated and amplified by AI systems, leading to discriminatory outcomes or reinforcing existing inequalities. Addressing algorithmic bias requires careful evaluation and testing of AI models with diverse datasets, as well as ongoing monitoring and updating to ensure fairness.

There can be challenges in integrating human and AI workflows seamlessly. Human workers may have their own established ways of working and collaborating which differ from the automated processes introduced by AI systems. Harmonizing these workflows requires careful planning and coordination, as well as providing training and support for employees to adapt to new ways of working with AI tools.

Strategies for Successful Implementation of Human-AI Collaboration

Successful implementation of human-AI collaboration in cloud environments relies on a combination of strategies that foster effective communication, understanding, and trust between humans and AI systems. One key strategy is ensuring clear roles and responsibilities for both humans and AI within the collaborative workflow. By defining specific tasks and areas where each party excels, it becomes easier to establish a harmonious working relationship between human expertise and the capabilities of AI systems.

Another important strategy for successful human-AI collaboration is continuous evaluation and feedback. Regularly assessing the performance of AI systems allows for necessary adjustments to be made in order to improve their accuracy, efficiency, and effectiveness. Additionally, gathering feedback from human collaborators helps identify any limitations or challenges they may face when working alongside AI technologies. This feedback can then be used to refine the design of future collaborations, making them more seamless and productive.

Fostering a culture that embraces experimentation is vital for successful implementation of human-AI collaboration. Encouraging curiosity among both humans and AI enables exploration of different possibilities, innovative problem-solving approaches, and adaptation to evolving needs. Furthermore, creating an environment that closely monitors performance metrics can facilitate learning from successes as well as failures. Such an agile environment encourages continuous improvement in collaborative workflows by identifying opportunities for optimization while minimizing risks.

By following these strategies – clear role delineation, evaluation with feedback loops, and nurturing experimental culture – businesses can make significant strides towards harnessing the true potential of human-AI collaboration in cloud environments.

Conclusion: Embracing the Future of Work with Human-AI Collaboration

In conclusion, the future of work lies in the collaboration between humans and AI. While there may be concerns about job displacement and machines taking over human roles, it is important to understand that AI technologies are not meant to replace humans but to augment their capabilities. By embracing this collaboration, we can unlock new levels of efficiency and productivity.

One of the key advantages of human-AI collaboration is the ability to redefine workflows in cloud environments. With AI algorithms handling mundane and repetitive tasks, employees can focus on more complex and creative work that requires critical thinking and problem-solving skills. This shift in workflow allows for a more fulfilling work experience as employees can engage in strategic decision-making rather than getting bogged down by routine tasks.

Human-AI collaboration opens up opportunities for innovation and growth. By leveraging AI systems' abilities to analyze vast amounts of data quickly and accurately, businesses can gain valuable insights that would have otherwise been overlooked or take much longer to uncover. These insights can drive informed decisions, help identify new trends or gaps in the market, and ultimately give businesses a competitive advantage.

Ultimately, embracing the future of work with human-AI collaboration requires a shift in mindset from fearing automation to seeing it as an opportunity for progress. As technology continues to advance rapidly, businesses need to adapt their strategies accordingly and find ways to leverage these advancements for greater success. The key lies in finding a balance between utilizing AI technologies while still harnessing the unique skills and perspectives that humans bring to the table.

The post Human-AI Collaboration in Cloud Environments: Redefining Workflows appeared first on Datafloq.

Artificial intelligence (AI) is an integral part of our everyday life as it makes our life simple and easy since it transforms our lives in many ways. Among the sectors that have witnessed this catalytic change is the healthcare sector that's transforming lives through medical treatment.

AI is a great tool in healthcare diagnostics and monitoring of patients. Integration of AI in the operating room is the next step in the implementation of AI-based systems as machine learning in medical care will be of great benefit to surgeons as well as patients.

Robotics is one of the key aspects of AI that's impacting our daily lives, as it's a combination of electrical engineering, mechanical engineering, and computer science and engineering. Robots have a great deal of similarity with humans as they can perform like humans when enabled with AI.

So, let's look at how robots are transforming the healthcare sector. Robots can be used to:
1. Carry out operations in an accurate manner
2. Provide therapy to patients
3. Used as prosthetic limbs

Role of AI in Transforming Healthcare

1. Learning from large datasets

A significant number of years are invested by specialists in refining and becoming proficient in their skill sets. Physicians oversee several surgical procedures for learning different techniques and applying the best methods in their practice, however, they feel restrained due to human limitations. AI-based systems can absorb vast amounts of information within seconds. Robots in surgery can be trained with AI for maximizing their capability in utilizing information.

Several recordings of surgeries can be loaded within seconds in AI-based systems as there are no time or memory constraints. The robots are capable of remembering the first procedure with great precision right to the last. AI helps in educating physicians on various methodologies. It assists physicians in reshaping their learning and practicing skills for perfecting their surgical skills.

2. Standardized practices

AI equips surgeons with a new outlook by introducing new methods to prevailing surgical practices resulting in standardized practices. Once data analytics is collected from different parts of the world, AI can gather different images, notice microscopic changes, and bring in new trends. Gathering knowledge from various surgeries, AI-based systems can help in discovering the best surgical techniques which were never discovered.
Patterns and trends detection can help in reshaping the way some procedures are performed offering surgeons and patients quality outcomes. Hence, practices will be standardized as surgeons globally will be able to follow similar methods for reaching optimal results.

3. Relieve cognitive and physical stress

Robotic surgery can be enhanced through AI by taking away the surgeon's stress. By using tools, monitoring operations, and sending alerts, AI can ensure a guided surgical procedure using a streamlined process. Surgeons can be freed from cognitive stress and operating time by performing a vast volume of surgical procedures with a high level of favorable outcomes.

4. Improving the ergonomics of operating rooms

The outlook towards improving the ergonomics of operating rooms can be transformed using AI. AI can help in identifying and suggesting ergonomically smarter solutions for alleviating physical stress during operations. AI along with smart robotic surgery helps surgeons from losing out on their physical health and lengthen their careers.

5. Redefining surgical care

As of date, two-thirds of the world population cannot access surgical treatment. AI-based systems can be teamed up with robotics to bridge this gap and ensure that patients globally are able to receive the quality surgical care that they deserve. AI will enable more and more physicians to access learning options from the best models in their field and assist them in performing surgeries.

6. Widening reach

Irrespective of the location and resources that surgeons have access to globally, surgeons can learn and use AI-based robotics to cater to a larger patient population. Surgeons who perform only one surgical procedure can widen their impact using a new tool for addressing a wide range of sub-specialties.

Summing up

As can be seen above, AI robotics is increasingly disrupting and transforming the healthcare market. It is mainly used for tracking patients' health conditions and creating a continuous supply chain of medication and other necessary items around the hospital. It is used for designing customized health tasks for patients.

Robotics is playing a vital role in the healthcare industry as it offers robots for assistance, provides accurate diagnosis, and lends remote treatment options. Analyzing robots helps in detecting even trivial patterns in a patient's health graph.

Robots led by machine learning play an active role in hospitals for carrying out micro-surgeries like unclogging blood vessels. AI robotics plays a critical role in providing treatment in remote locations for robots can single-handedly take on many clinical tasks, for example, the bot-pill which is a discovery of AI robotics.

AI goes hand-in-hand with robotic surgery. Integration of AI-based systems with medical technology is detrimental to enhancing both surgeon and patient experiences.

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