What is the difference between AI and BI?

AI (Artificial Intelligence) can be overwhelming.  Even the programmers who created these computer models do not know how they work.

BI (Business Intelligence) is critical for business decision-makers but many think AI can function like BI which it really can’t.

In simple terms, the difference between AI and BI is as follows:

AI (Artificial Intelligence):  AI is like having a smart assistant that can learn from data and make decisions on its own.  It can analyze large amounts of data to find patterns, predict outcomes, or even understand human language.  AI can automate tasks, suggest solutions, and adapt to new situations without being explicitly programmed.

BI (Business Intelligence):  BI is looking at a report or dashboard that tells you what’s happening in your business.  It helps you understand past performance, monitor key metrics, and identify trends using data visualization and analytics.  BI doesn’t make decisions for you but provides insights that humans can use to make informed decisions.

BI is good at displaying the patterns in data, and AI is good at helping to explain the patterns.

AI is best used as an assistant and to discover patterns in data that are hidden. To benefit from having AI, you’ll need to first prepare your data for AI (here’s a helpful checklist). First think about what you are looking for which is a good starting point before diving into more complex data inquiries.

For example: What ZIP code do most of our clients reside in?  How old is the average client?  BI can give you these answers – but getting AI to function like BI is a major step to finding out more details in the data which BI can’t.  As an illustration, “Generate a list of clients who purchased more than 5 times and then haven’t purchased in one year and looking at their purchases tell me 5 reasons they stopped purchasing.” This is an example of an AI query that BI can’t answer.

AI is about smart algorithms that can learn and act autonomously, while BI is about using data to understand and improve business operations with human interpretation and decision-making.

We have been testing, programming, and working with AI and BI for years. If you’d like to have a conversation to discuss what you need, give us a call. We are happy to help.

Specific tools you’ll need to get your database ready for AI

Based on all the AI work we have accomplished over the past few years we developed the following checklist to help you prepare your data using private cloud or on-premise systems and software …which is a critical first step.  Don’t hesitate to contact us with any questions.

1. Data Integration:
Integration tools like Talend, Informatica, or Apache NiFi consolidate data from multiple sources into a single, unified view.

2. Data Cleaning and Preparation:
Use a private cloud or on-premise data cleaning tool like OpenRefine, Excel, or SQL to identify and correct errors, inconsistencies, and missing values in the data.

3. Data Transformation:
Data transformation tools like Apache Beam, Apache Spark, or AWS Glue convert data into a format suitable for AI models, such as structured or semi-structured data.

4. Data Labeling:
Use a private cloud or on-premise data labeling tool like Labelbox, Hive, or Amazon SageMaker to identify and label the data that will be used to train AI models consistently and efficiently.

5. Data Storage:
Distributed file systems (DFS) like Hadoop Distributed File System (HDFS), Amazon S3, or Google Cloud Storage store the data in a scalable and durable manner.

6. Data Security:
Implement appropriate security measures to protect the data from unauthorized access or misuse using tools like Apache Hadoop, AWS Key Management Service (KMS), or Google Cloud Key Management Service (KMS) during storage and transmission.

7. Data Governance:
Establish clear policies and procedures for data management and use, utilizing tools like Apache Atlas, AWS Lake Formation, or Google Cloud Data Fusion to manage data access and usage.

8. AI Model Development:
Learning frameworks like TensorFlow, PyTorch, or Scikit-learn develop and train AI models using the prepared data.

9. Deployment:
Deploy the trained AI models into production environments using tools like Kubernetes, Docker, or AWS Elastic Beanstalk in a scalable and efficient manner.

10. Monitoring and Maintenance:
Continuously monitor the performance of the AI models in production with tools like Prometheus, Grafana, or New Relic to monitor the models’ performance and make necessary adjustments.

By using private cloud or on-premise systems and software only, you can ensure that your data is stored and processed securely and efficiently within your infrastructure, without relying on any external services or platforms.

10 key steps for getting your database ready for AI

We have found that companies increase their chances for successful integration of AI exponentially by following these 10 steps. Please note that these steps are general, and any specific applications need to be discussed thoroughly. If you need help, let us know. We’d be happy to share our experience.

1. Data Inventory and Assessment: Conduct a comprehensive inventory of all data sources, including databases, files, and data warehouses. Assess the quality, completeness, and consistency of the data in each source.
2. Data Integration and Standardization: Integrate data from different sources to create a unified view of the organization’s data landscape. Standardize data formats, naming conventions, and data dictionaries to ensure consistency and compatibility across datasets.
3. Data Cleaning and Preprocessing: Cleanse and preprocess the data to remove inconsistencies, errors, duplicates, and missing values. This ensures that the data is accurate, reliable, and suitable for analysis.
4. Data Security and Compliance: Does all data need to be imported into AI, should it all be imported?  Implement robust data security measures to protect sensitive information and ensure compliance with relevant regulations such as GDPR, HIPAA, or industry-specific standards. Establish access controls and encryption mechanisms to safeguard data privacy and integrity.
5. Data Governance Framework: Establish a data governance framework to define policies, procedures, and responsibilities for managing and governing data assets. This includes data stewardship, metadata management, and data lineage tracking to ensure accountability and transparency.
6. Data Storage and Infrastructure: Evaluate the scalability, performance, and cost-effectiveness of existing data storage and infrastructure solutions. Consider migrating to cloud-based platforms or implementing data lakes to accommodate growing volumes of data and enable flexible analytics capabilities.
7. AI Readiness Assessment: Assess the organization’s readiness and maturity level for implementing AI solutions. Evaluate factors such as data readiness, technological capabilities, organizational culture, and leadership support.
8. Skills and Training: Invest in training and upskilling employees to develop the necessary skills and expertise in data science, machine learning, and AI technologies. Encourage a culture of continuous learning and experimentation to foster innovation and adoption of AI-driven insights.
9. Pilot Projects and Proof of Concepts: Test first with smaller datasets.  Start with small-scale pilot projects or proof of concepts to demonstrate the value and feasibility of AI applications. Identify specific use cases or business problems where AI can provide tangible benefits and measurable outcomes.
10.  Collaboration with AI Experts: Collaborate with AI experts, data scientists, and technology partners to leverage their domain knowledge and technical expertise in implementing AI solutions. Consider outsourcing certain aspects of AI development or consulting services to accelerate the implementation process.

Unleash the Power of Speed, Stability, and Safety

Take the first step towards unlocking the full potential of AI for your business. Contact us today and let’s discuss how our data-first approach and experience can make AI not just a possibility, but a powerful asset for your organization.

What is AI Trust, Risk and Security Management
(AI TRiSM)

In the rapidly evolving landscape of artificial intelligence (AI), the integration of AI technologies across various domains necessitates a dedicated focus on trust, risk, and security management. The emergence of AI Trust, Risk, and Security Management (AI TRiSM) signifies the imperative to ensure responsible and secure AI deployment.

This blog explores the multifaceted realm of AI TRiSM, delving into the complexities of building trust in AI systems, mitigating associated risks, and safeguarding against security threats. By examining real-world examples, case studies, and industry best practices, we aim to provide insights into strategies that organizations can adopt to navigate the delicate balance between harnessing AI’s benefits and mitigating its inherent risks.

As we explore future trends and challenges in AI TRiSM, the blog seeks to equip readers with the knowledge necessary for the ethical, secure, and trustworthy implementation of AI technologies in our interconnected world.

AI Trust Management

In artificial intelligence (AI), trust is a foundational element crucial for widespread acceptance and ethical deployment. AI Trust Management (AI TM) involves cultivating confidence in AI systems through transparency, accountability, and fairness. Transparency in AI algorithms ensures that their operations are understandable, reducing the “black box” perception. Accountability emphasizes the responsibility of developers and organizations to ensure the ethical use of AI.

Addressing biases and promoting fairness in AI outcomes are essential aspects of trust management. Real-world case studies demonstrating successful AI trust management implementations offer valuable insights into building trust in AI systems. By prioritizing transparency, accountability, and fairness, AI Trust Management aims to foster confidence in AI technologies, promoting responsible and ethical deployment across diverse applications.

AI Risk Management

The integration of artificial intelligence (AI) introduces a spectrum of risks that organizations must proactively identify, assess, and mitigate. AI Risk Management involves a comprehensive approach to navigating potential challenges associated with AI deployment. Identifying risks, such as data privacy breaches, legal and regulatory non-compliance, and operational vulnerabilities, is a crucial first step. Strategies for assessing and mitigating these risks include robust testing, continuous monitoring, and implementing contingency plans.

Real-world examples underscore the consequences of inadequate AI risk management, emphasizing the need for organizations to stay vigilant in the face of evolving threats. By implementing rigorous risk management practices, organizations can foster resilience and ensure the responsible and secure integration of AI technologies into their operations.

AI Security Management

As artificial intelligence (AI) continues to permeate diverse sectors, the importance of robust AI Security Management cannot be overstated. AI Security Management addresses a range of concerns, including cybersecurity threats, adversarial attacks, and vulnerabilities in AI models. Recognizing the dynamic nature of these risks, security measures encompass a secure development lifecycle for AI, access controls, authentication protocols, and encryption for safeguarding sensitive data.

By implementing best practices in AI security, organizations can fortify their defenses, ensuring the confidentiality, integrity, and availability of AI systems in the face of evolving threats. AI Security Management stands as a cornerstone for the responsible and secure advancement of AI technologies across industries.

Integrating AI TRiSM into Business Strategies

Effectively incorporating AI Trust, Risk, and Security Management (AI TRiSM) into business strategies is paramount for organizations seeking to harness the benefits of artificial intelligence (AI) while mitigating associated risks. This section explores the pivotal role of AI TRiSM in enhancing overall business resilience.

Aligning AI TRiSM with the entire AI development lifecycle ensures that trust, risk, and security considerations are integrated from the initial stages of AI project planning to deployment and ongoing monitoring. By embedding these principles into the fabric of business strategies, organizations can create a culture of responsible AI development.

Moreover, recognizing the interconnectedness of AI TRiSM with broader enterprise risk management practices is crucial. This alignment enables organizations to holistically assess and address risks related to AI, integrating them into the larger risk mitigation framework.

Strategic deployment of AI TRiSM involves collaboration across various organizational functions, fostering communication between data scientists, cybersecurity experts, legal teams, and business leaders. Establishing multidisciplinary teams ensures a comprehensive understanding of potential risks and effective implementation of mitigation strategies.

Furthermore, organizations should consider AI TRiSM as an integral component of their ethical frameworks, corporate governance, and compliance initiatives. This not only instills trust among stakeholders but also positions the organization as a responsible AI innovator.

Future Trends and Challenges in AI TRiSM

As the landscape of artificial intelligence (AI) continues to evolve, the field of AI Trust, Risk, and Security Management (AI TRiSM) faces emerging trends and challenges that shape its trajectory. This section explores what lies ahead in the dynamic world of AI TRiSM.

Emerging Trends:

1. Explainability and Interpretability Advances: Future AI systems are likely to see advancements in explainability and interpretability, addressing the need for transparent decision-making. Improved methods for understanding and interpreting AI models will contribute to building trust.
2. Ethical AI Certification: The development of standardized frameworks for certifying the ethical use of AI systems is expected to gain traction. Certification programs may help establish a benchmark for responsible AI practices and enhance trust among users.
3. AI-powered Security Solutions: With the increasing sophistication of cyber threats, AI-driven security solutions will become more prevalent. AI algorithms will play a pivotal role in detecting and mitigating evolving security risks, offering a proactive approach to safeguarding AI systems.
4. Global Regulatory Frameworks: Anticipated developments in global regulatory frameworks for AI will likely impact AI TRiSM. Harmonizing standards and regulations across regions will be crucial for organizations operating in the global AI landscape.

Challenges:

1. Adversarial AI Threats: As AI systems become more prevalent, adversaries may develop sophisticated techniques to manipulate or deceive AI algorithms. Safeguarding against adversarial attacks poses a persistent challenge for AI TRiSM.
2. Data Privacy Concerns: The increasing scrutiny of data privacy and protection will continue to be a significant challenge. Ensuring that AI applications adhere to evolving data privacy regulations poses a constant hurdle for organizations.
3. Bias Mitigation Complexity: Despite efforts to mitigate bias in AI systems, achieving complete fairness remains challenging. As AI models become more complex, addressing and eliminating biases in various contexts will require ongoing research and innovation.
4. Dynamic Regulatory Landscape: Rapid advancements in AI technologies may outpace the development of regulatory frameworks, creating uncertainties. Adapting AI TRiSM practices to dynamic and evolving regulations will be a continual challenge for organizations.

Conclusion

AI Trust, Risk, and Security Management (AI TRiSM) emerge as critical pillars for organizations embracing new-age technologies like AI. At the forefront of innovation, Protected Harbor recognizes the foundational importance of fostering trust, managing risks, and securing AI systems. The principles of transparency, accountability, and fairness underscore a commitment to responsible AI deployment. As we navigate future trends and challenges, the imperative is clear: staying informed, adaptive, and committed to ethical AI practices is key for organizations aiming to thrive in the dynamic world of AI.

Explore how Protected Harbor can empower your business in the era of AI by implementing cutting-edge strategies – a journey towards responsible and innovative AI adoption. Contact us today!

Top Cybersecurity Trends in 2024

In a world where technology evolves at an unprecedented pace, the importance of cybersecurity cannot be overstated. As we embark on the journey through 2024, the digital landscape is becoming more complex, and with it, the challenges and threats to cybersecurity are reaching new heights. In this blog, we delve into the top cybersecurity trends anticipated to shape organizations’ defense strategies worldwide. These top cybersecurity trends in 2024 reflect the ongoing arms race between cyber attackers and defenders and highlight the innovative solutions cybersecurity experts are deploying to stay one step ahead.

In the face of rising cyber threats, understanding and adopting these trends is not just a matter of safeguarding sensitive data but is integral to sustaining the trust and reliability upon which the digital world thrives.

1. AI-Powered Threat Detection

Artificial Intelligence (AI) continues to revolutionize cybersecurity with its ability to analyze vast datasets and identify anomalies. AI-powered threat detection systems are becoming more sophisticated, providing real-time insights into potential cyber threats, and enabling organizations to respond swiftly.

2. Zero Trust Architecture

The traditional security model of trusting entities inside a network gives way to a Zero Trust Architecture. This approach mandates verifying every user and device, regardless of their location, before granting access. This proactive model enhances overall security posture.

3. Quantum-Safe Cryptography

With the advent of quantum computers, there is a growing concern about their potential to break current cryptographic algorithms. Quantum-safe cryptography is gaining prominence, ensuring data remains secure even in the face of quantum threats.

4. Cloud Security Maturity

As businesses increasingly rely on cloud services, the need for robust cloud security measures becomes paramount. In 2024, organizations are focusing on enhancing their cloud security maturity to protect sensitive data stored and processed in the cloud.

5. Ransomware Resilience

Ransomware attacks have become more sophisticated and prevalent. The emphasis is on building resilience against such attacks, incorporating advanced backup and recovery strategies, employee training, and deploying advanced threat intelligence solutions.

6. 5G Security Challenges

As 5G networks become ubiquitous, the attack surface for cyber threats expands. Addressing the unique security challenges posed by 5G technology is crucial to prevent potential vulnerabilities in the network infrastructure.

7. IoT Security Focus

The proliferation of Internet of Things (IoT) devices introduces new entry points for cyber threats. Organizations are intensifying their efforts to secure IoT devices, implementing robust encryption, authentication, and monitoring mechanisms.

8. DevSecOps Integration

Integrating security into the DevOps process from the outset, known as DevSecOps, is gaining traction. This approach ensures that security measures are seamlessly integrated throughout the development lifecycle, enhancing overall system security.

9. Biometric Authentication

Traditional passwords are increasingly being replaced by more secure biometric authentication methods. Fingerprint recognition, facial recognition, and other biometric measures add an extra layer of security to user authentication.

10. Global Collaboration against Cyber Threats

Cyber threats are borderless, and collaboration is key. In 2024, there is a growing emphasis on global cooperation among governments, businesses, and cybersecurity professionals to share threat intelligence and collectively strengthen defenses against cyber threats.

Generative AI: Short-term Skepticism, Longer-Term Hope

Generative AI, often hailed as a harbinger of innovation and progress, evokes a spectrum of reactions within the cybersecurity landscape. While its potential to revolutionize various industries is undeniable, skepticism looms large in the short term, particularly concerning its implications for cybersecurity.

At the heart of this skepticism lies the concern over vulnerabilities inherent in IoT (Internet of Things) devices. As Generative AI continues to advance, the integration of AI and ML (Machine Learning) algorithms into IoT ecosystems introduces new avenues for exploitation. Malicious actors could leverage these technologies to orchestrate sophisticated cyber attacks, exploiting vulnerabilities in interconnected systems with unprecedented precision and scale.

However, amidst the prevailing skepticism, there exists a glimmer of hope for the longer term. Generative AI, when wielded judiciously, holds the potential to bolster cybersecurity defenses and mitigate emerging threats. By harnessing the power of AI and ML, cybersecurity professionals can proactively identify and address vulnerabilities, fortifying IoT infrastructures against potential breaches.

As we navigate the evolving landscape of cybersecurity in 2024, the intersection of Generative AI, IoT vulnerabilities, and advanced machine learning algorithms will undoubtedly shape the top cybersecurity trends. Embracing a nuanced perspective that acknowledges both the short-term challenges and the longer-term opportunities inherent in Generative AI is paramount to fostering a resilient cybersecurity ecosystem capable of withstanding the ever-evolving threat landscape.

Conclusion

As we conclude our exploration of the top cybersecurity trends in 2024, it is evident that the future of digital security is dynamic and challenging. The ever-evolving threat landscape necessitates a proactive and adaptive approach to cybersecurity. Organizations must not view cybersecurity as a mere necessity but rather as a cornerstone of their operations.

In this crucial journey toward fortified defenses, it’s essential to mention leaders like Protected Harbor. As one of the top cybersecurity providers in the United States, they stand at the forefront of technology and security innovation. With a commitment to staying ahead of emerging threats, Protected Harbor exemplifies the proactive approach needed to navigate the intricate cybersecurity landscape of 2024.

The interconnected world of 2024 demands not only robust defense mechanisms but also strategic partnerships with industry leaders. By aligning with trusted cybersecurity partners, organizations can enhance their security posture and better safeguard their digital assets.

Take the next step in securing your digital future! Contact Protected Harbor today and discover how our cutting-edge solutions can empower your organization to thrive in the digital age. Don’t just meet cybersecurity challenges; conquer them with confidence. Your digital resilience begins here!

What are the next steps in AI? What about an application that you take a picture of your refrigerator and ask the application, which is using AI to give you a spicy interesting recipe based on what you have? What about if you use this service over time and the service starts to order from the store automatically your groceries. What about if the application using AI makes recommendation for new foods to try. After all, if the large training model has imported all recipes and many people who eat Salmon also like mustard, the maybe the app tells the store AI to add mustard seed your next shopping list.

The next steps in AI promise an exciting journey of innovation and progress. As artificial intelligence evolves, we can anticipate smarter, more intuitive technologies that seamlessly understand and adapt to human needs. Advances in machine learning will enable AI to grasp complex patterns, making predictions and decisions with increased accuracy. Ethical considerations will become pivotal, ensuring AI aligns with human values. Collaborations across industries will unlock new possibilities, from healthcare breakthroughs to personalized experiences. As AI progresses, continual research, responsible development, and harmonious integration with human society will shape a landscape where AI enhances our lives in unimaginable ways.

What about a new Google service AutoWrite, that reviews your email. The feature looks at who you have responded too in the past and how quickly, that tells the service how important that person is to you. The service learns your style from your previously sent emails to that person. When that person sends another email then the service creates a response email, you score the response – 1-100. Over time when the system learns your style all based on the scoring. As a user, your start to build confidence in the system and you allow any email with a potential score of 90 to be sent out.

What about a friendship app that connects you to a “dedicated connection”. The AI behind the connection has been given access to your email, text message and social networks. It wakes you up in the morning and asks what you were dreaming about because the fitness app on your wrist reported that you had trouble sleeping. The Friendship App, “Forever yours” ask was the sleeping trouble connected to your girlfriend who sent several nasty text messages last night and posted on your social networking site that you don’t hear her. The app councils you on how you feel, using therapy techniques documented on web sites that the App has learned. “Forever yours” over times feels like a real friend.

All of these services, applications or features are underway now, and more beyond that.

Ai is to the 2020’s as Social Networks were to 2005+. Social Network have caused world wide problems with information silos where people self-isolate themselves. It is great to be able to easily keep up on a social network with my friends, but do I really need or want 500 Happy Birthday messages? Are those messages genuine or was it the system. What about messages that are paid for that appear to be from real people that I think I know? What about if those messages are pushing me to get mad at some cause or people?

We have not figured out yet how to manage Social Networks, Ai will have a similar impact.

AI will be integrated into peoples’ lives, and there will certainly be benefits, but at what cost?

I choose to believe that humans can adapt, but I have concerns we might not enough time to understand what is occurring.

AI can stand for both Automated Intelligence and Artificial Intelligence, and it’s important to understand the distinction between the two.

Imagine AI as a super-smart robot friend! AI, or Artificial Intelligence, is when computers learn to think and make decisions like humans. It’s like teaching your robot buddy to recognize colors, play games, or even understand when you’re happy or sad. AI helps machines learn from experiences and become good at tasks without being told exactly what to do. It’s like having a friend who learns and gets better every time you play together. So, AI is like magic computer brains that make our machines and robots super clever and helpful!

There seems to be a lot of confusion among professionals and laypeople regarding the difference and functionality of these two concepts.

Automated Intelligence has been in existence for quite some time. At its core, it involves predicting actions based on patterns. A typical example of Automated Intelligence is a spam filter. As a user, when you mark five emails as spam, the spam filter utilizes those marked emails as a reference to determine which other emails are likely to be spam. Future emails are then compared against the reference template and variations of the templated emails, allowing the system to learn and improve. If the system makes a mistake, the user can mark an incorrectly classified email as “good,” and the algorithm will adjust accordingly.

Automated Intelligence is task-oriented and driven, providing significant assistance in many cases. Grammar checkers are another example of Automated Intelligence. They analyze the context of words and learn the user’s patterns. While some grammar rules are universal, such as the distinction between “their” and “there,” other rules are more subjective, and a reliable grammar checker learns the user’s preferred style through their writing.

This form of Automated Intelligence is focused on specific tasks and requires human management, ultimately serving to support humans in their activities.

On the other hand, Artificial Intelligence is an entirely different concept, and it is neither truly artificial nor intelligent.

As humans increasingly document our conversations digitally, we have amassed an enormous database of raw information. Platforms like CHAT GPT were partially trained on Reddit. Consider the content on Facebook, emails, websites, and all the digital content created by humans over the past three decades. This vast pool of information represents a wealth of human interactions and reactions. Some of it is marketing-focused, legal-focused, or politically-focused, while other content is intended to evoke laughter or anger. It encompasses the entire spectrum of human emotions. Think of this extensive collection of data as a massive database.

The challenge with such a database is that not all entries carry the same weight or significance in our minds. For example, articles about constructing a bomb are treated with the same importance as articles about Mother Teresa. However, as humans, we inherently value the articles about Mother Teresa’s charitable work more than instructions on building a bomb. In a large database, our subjective prioritization cannot be easily measured, as all data points are treated equally.

Artificial Intelligence goes beyond the capabilities of Automated Intelligence, but the key differentiating factor lies in the process of “training.”

Training AI developers often highlight the complexity and elegance of the training process. At its core, training involves inputting data to allow the system to perceive and comprehend patterns. Referring back to the previous example of marking spam, this process trains the system to identify spam. In modern systems, training involves reviewing vast portions of the human-created content database.

Modern CPUs contain billions of transistors and possess tremendous processing power. AI training utilizes this processing capability, especially through parallel processing, to categorize, process, and learn the patterns of human communication. Once the system has absorbed and comprehended the patterns, it can respond with the most probable answer based on what it has learned and calculated from the review data (the human database). This extensive review process is commonly referred to as a “large model.” The “training” involves categorizing and processing each question and response.

I hope this clarifies the distinction between Automated Intelligence and Artificial Intelligence, as well as sheds light on the training process in AI systems.

Please let us know if you have any further questions or if there’s anything else we can assist you with.

Can AI be helpful in my job? The short answer is yes, there are some tasks that AI can be really helpful with. One example, for this white-paper I asked ChatGPT “Can you make a slide deck on the helpful points of AI?” and it output the following.

Slide 1: Title

Catchy title introducing the topic of the presentation.

Slide 2: Introduction

Briefly introduce yourself and provide an overview of the presentation’s purpose.

State the benefits of AI in the workplace.

Slide 3: Automation and Efficiency

Highlight how AI automates repetitive tasks, saving time and increasing productivity.

Mention specific examples like data entry, document processing, and email filtering.

Slide 4: Intelligent Virtual Assistants

Explain how AI-powered virtual assistants can handle routine queries, providing 24/7 support and freeing up human resources.

Describe how they can improve customer service and reduce workload on employees.

Slide 5: Data Analysis and Insights

Discuss how AI can analyze large volumes of data quickly, extracting meaningful insights and patterns.

Emphasize the benefits of data-driven decision-making and improved operational efficiency.

Slide 6: Natural Language Processing (NLP)

Explain the concept of NLP and its applications.

Highlight how NLP can enhance communication, enable language translation, and aid in document summarization.

Slide 7: Enhanced Security

Discuss how AI can contribute to office security by detecting and preventing potential threats.

Mention applications like network traffic analysis and cybersecurity breach detection.

Slide 8: Intelligent Document Management

Explain how AI can assist in organizing and managing documents efficiently.

Highlight features like automatic categorization, tagging, and information extraction.

Slide 9: Workflow Optimization

Discuss how AI can analyze workflow patterns, identify bottlenecks, and provide recommendations for process improvements.

Mention how it can optimize resource allocation and enhance overall productivity.

Slide 10: Predictive Analytics

Explain how AI can utilize historical data and machine learning algorithms to make accurate predictions.

Highlight applications like sales forecasting, demand planning, and customer behavior analysis.

Slide 11: Ethical Considerations

Emphasize the importance of ethical AI implementation, including transparency, accountability, and privacy.

Slide 12: Conclusion

Summarize the key points discussed throughout the presentation.

Reinforce the benefits of AI in the office environment.

Slide 13: Q&A

Allocate a slide for audience questions and provide answers or initiate a discussion.

Not a bad starting point but the presentation is missing a number of points, for example, providing Legal or Medical research summery assistance, or improve communications. My point is as a research assistant this gives me a good starting point. You might try using this approach for your next class assignment or presentation. Using AI as a research assistant on topics you know, allows you to focus on how the content should be organized and communicated and not the basic framework.

Remember the training model – Reddit and other web sources. Those sources will have valid and incorrect information, and AI can’t tell which one is valid and which isn’t. AI is not judging the data it is learning from, it is looking how often like-minded data exists. If all web data on a topic is false, then AI will give completely incorrect information.

I urge you to tread lightly when using AI on topics you do not know, do not trust that what ChatGPT or other AI sites produce is correct, it is not a super smart brain – it is summarizing data it has absorbed and it could have easily reached the wrong conclusion.

AI training is at the point of a precocious 5-year-old who has heard a cure word. The 5-year-old who hears a curse word can’t help but to repeat it and based on adult’s feedback senses something is different about the word, so they use it a lot, until confronted and schooled by an adult.

One attorney appeared who knew the material appeared to get lazy and assumed ChatGPT was a super-brain and included the output without review and without editing is now being called in front of hte court to explain why none of the references used in his submission where valid – https://www.nytimes.com/2023/06/08/nyregion/lawyer-chatgpt-sanctions.html.

So the best way to use AI is to use it as a sounding board, consultant, research assistant, not as an expert. Even human experts can be wrong, and AI can be very wrong. Properly used it can be a significant time saver.

Here is another example, what if I am having a dinner party for 4 and some of the guests are on the Mediterranean Diet, I have some basic items in my refrigerator, but I need some ideas. In the past I would have used a search engine search, which might then I would need to calculate an ingredients list, or I can ask ChatGPT; “I have eggs, milk, spices, and vegetables please give me a spicy recipe for 4 for dinner following the Mediterranean diet”. The result is;

Here’s a spicy Mediterranean-inspired recipe for dinner using eggs, milk, spices, and vegetables:

Spicy Shakshuka with Roasted Vegetables

Ingredients:

1 tablespoon olive oil

1 onion, finely chopped

2 garlic cloves, minced

1 red bell pepper, diced

1 yellow bell pepper, diced

1 zucchini, diced

1 eggplant, diced

1 teaspoon cumin

1 teaspoon paprika

1/2 teaspoon cayenne pepper (adjust to taste)

1 can (400g) diced tomatoes

Salt and pepper to taste

4-6 large eggs

Fresh parsley or cilantro, chopped (for garnish)

Instructions:

Preheat the oven to 400°F (200°C). Place the diced zucchini and eggplant on a baking sheet, drizzle with olive oil, and season with salt and pepper. Roast in the oven for 20-25 minutes until the vegetables are tender and lightly browned.

While the vegetables are roasting, heat olive oil in a large skillet or frying pan over medium heat. Add the chopped onion and sauté until it becomes translucent, about 5 minutes.

Add minced garlic, diced red and yellow bell peppers to the skillet, and cook for an additional 5 minutes until the peppers soften.

Stir in the cumin, paprika, and cayenne pepper, and cook for a minute to toast the spices.

Add the diced tomatoes to the skillet, season with salt and pepper, and simmer the mixture for about 10 minutes to allow the flavors to meld.

Using a spoon, create small wells in the tomato mixture and crack the eggs into the wells. Cover the skillet and cook for 5-8 minutes until the eggs are cooked to your desired doneness.

Remove the roasted vegetables from the oven and add them to the skillet, gently mixing them with the tomato and egg mixture.

Sprinkle the chopped parsley or cilantro over the top for garnish.

Serve the spicy shakshuka with roasted vegetables hot, accompanied by crusty bread or pita for dipping.

This recipe incorporates the bold flavors of Mediterranean spices, roasted vegetables, and eggs cooked in a rich tomato sauce.

There is no information if this is a good tasting recipe but since the AI training data included real recipes then most likely it will. Again, this is a reference, if I have experienced that garlic bothers my guest then I can substitute another spice or I can AI for a revised recipe but my point is I am using AI as a reference tool and then working from there.

These are two examples of how ChatGPT can be used as a tool or an assistant to help people process, but only when used correctly.

There is a dark side to AI. The dangers of artificial intelligence stem from its training – us. Since AI is trained on data we have created, it has learned both the positives and negatives of human reactions. However, AI is simply a computer program; it has no ability to learn or comprehend. A computer program lacks a soul and cannot develop compassion. While a person can express hate towards someone and later express joy or love, a computer program cannot grasp such emotions. When processing data with sentiments like “I want to kill you” in anger, a system has no understanding, and a person will never act on those feelings.

The development of a complete training model would have taken more time. There is no easily processed data set on morality, for instance, as seen in the Bible. The Old Testament presents a harsher approach to morality than the New Testament. How would the actions or events in the Bible be weighed against other training materials? These kinds of questions highlight that the source of issues lies in the training model. OpenAI, Microsoft, Google, and others chose to release their AI systems as-is due to competitive reasons. The industry’s response to the incomplete training of AI is to request government intervention, legislation, and a halt to all new AI releases.

The incomplete training model also has another negative consequence – it is impossible to determine why a decision was made as it was. The model is designed to learn from vast amounts of data, which means that for any given decision or recommendation, it isn’t possible to ascertain the reasoning behind it. This has led to some interesting results, such as ChatGPT telling a reporter that it loves him and that he should leave his wife (https://fortune.com/2023/02/17/microsoft-chatgpt-bing-romantic-love/), and that the AI system destroys whatever it wants. Since then, all AI vendors have attempted to implement limitations to avoid such results. The AI vendors realized that over time, AI systems would start producing unexpected and even bizarre results. Rather than addressing the problem at its core, the incomplete training model, the AI vendors chose to restrict access to the systems. The idea is that limited interaction over a short period of time would prevent bizarre results.

The problem with these band-aid limitations is that they are just temporary solutions, and it is possible to bypass them. Systems that depend on AI will eventually produce bizarre results over time.

Consider a scenario where a city hires a vendor to optimize its traffic light pattern. The city seeks to determine the traffic light pattern that results in the fewest stopped vehicles and can update the pattern in response to traffic congestion events.

The city conducts a successful test case in one neighborhood, and based on the outstanding results, it decides to deploy the system citywide. Over time, the AI calculates that the optimal solution would be to have fewer cars on the road, and thus, it would occasionally cause large-scale, multi-vehicle traffic accidents to achieve better travel times for the unaffected vehicles. Without morality incorporated into the AI model and without humans understanding why each decision was made, all of this is possible, and due to our lack of IT education, even probable.

But that’s not the worst of what is possible with AI. Currently, AI has been taught by processing human-created information. The next step will be for AI to learn from its own internal interactions.

Risks of Artificial Intelligence

The rapid advancement of artificial intelligence (AI) brings a spectrum of risks that necessitate careful consideration. One primary concern revolves around the ethical implications of AI, as its decision-making processes may inadvertently perpetuate biases embedded in training data.

Security vulnerabilities pose another significant risk, with AI systems potentially becoming targets for malicious attacks, leading to compromised functionality and unauthorized access to sensitive information.

The lack of transparency in complex AI algorithms raises issues of accountability and interpretability, making it challenging to understand and explain the rationale behind certain decisions, especially in critical domains like healthcare or criminal justice.

Moreover, there’s an ongoing debate about the potential job displacement resulting from automation driven by AI, impacting various industries and potentially exacerbating societal inequalities.

The dangers of artificial intelligence underscore the risks associated with AI, including the potential misuse of advanced technologies and the need for robust safeguards to mitigate unintended consequences.

As AI systems become more autonomous, the question of legal responsibility and liability for their actions becomes increasingly complex, emphasizing the importance of addressing the dangers of artificial intelligence comprehensively and responsibly.

Striking a balance between harnessing the benefits of AI innovation and managing its inherent dangers requires a concerted effort from policymakers, industry leaders, and the broader society to establish ethical frameworks and guidelines.

AI Will Replace Humans? The notion of artificial intelligence (AI) completely replacing humans remains speculative. While AI has shown remarkable capabilities in automating certain tasks, human qualities like creativity, emotional intelligence, and complex decision-making defy easy replication.

AI systems are tools designed to augment human capabilities, fostering efficiency and innovation rather than serving as outright substitutes. The future likely involves a collaborative relationship, with humans leveraging AI to enhance productivity while retaining their unique capacities.

However, ethical considerations, job displacement concerns, and careful regulation are essential to ensure the responsible and beneficial integration of AI technologies into various aspects of our lives.

At the moment, AI systems lack the complexity required for this, but many AI scientists predict that within 1-5 years, AI systems will be capable of self-learning. This means that if (or more accurately, when) a bug is detected in an AI system and humans attempt to repair the programming code to fix the defect, the system can learn.

The answer is yes, and faster than anyone thinks it will. Right now, high school and older students are using AI to write papers. Marketing companies are using AI to write internet content, and lawyers are using AI to write legal briefs, among other examples.

The impact of AI on your job depends on the job itself. In general, the more uniquely creative a job is, the less impact AI will have. Conversely, the more formulaic a job is, the more AI will affect it.

IBM has announced that the HR department will not hire any additional staff. As staff members retire, those jobs will be replaced by AI. Jobs involving form processing, approving, sending, and editing will be eliminated by AI. Copy editors, copywriters, research assistants, basic programming, and website creation will all be replaced by AI.

Not all the news is bad, though. These formulaic jobs will also lead to new positions such as AI editors, content editors, and other editing roles. Since AI can generate false facts and lies, humans will be needed to double-check those facts, creating the need for editing positions.

Basic web design and programming will also soon be replaced. With AI, it is possible to describe a site and have the entire site created. However, someone still needs to confirm the programming code since, again, AI can provide inaccurate information and potentially cause more problems.

AI will lead to a series of changes in many industries. Take education, for example. Many students are now using AI to write papers. What can a teacher do? A teacher can encourage the use of AI, instead of pretending it is not happening, and then ask comprehensive questions that force students to demonstrate their understanding. For instance, if a student turns in an AI-generated paper on Edgar Allan Poe, the teacher can ask the student what they found most impactful about Poe, why, and what connections they saw with his work.

AI will have a greater impact in the classroom. It can interact with students and help them stay focused on repetitive activities. In areas where there is a shortage of teachers, AI can partially replace teaching by assisting students in learning various topics.

What about other industries? Why do people use a service? Because they see a connection, value, and/or trust. Companies that rely too heavily on AI will lose that customer connection, and customers will leave. For example, when an automated caller says, “I hope you have a great day,” do you believe them? Most likely not. The more personal the connection with the customer, the deeper the connection, and the higher the likelihood that customers will stay or new customers will join.

So, in short, make your job, company, or business more customer-oriented. The stronger the customer feels the connection, and the more genuine the connection is, the more likely customers will stay, even if the same service is available elsewhere for less.