Category Archives: AI

AI Will Take Your Job!? A Deeper Dive Beyond the Headlines

That’s right, unless you live under a rock, you can’t escape this rhetoric, but is it true? The media certainly would like to make you think so, wouldn’t they? I’m a bit less emphatic about the inevitable doomsday when the gap of human skill and machine capability dissolves. Candidly, I’m getting annoyed with said headlines, as it strikes me as alarmist and lazy. There are so many facets of human intelligence and capability that we haven’t been able to begin to understand, how are machines going to be able to go beyond that level of understanding when we are the ones who are creating machines in the first place? But hey, they need to sell ad space and headlines somehow, right? Why not send waves of concern through the uninformed to provide oodles of opportunity to publish article after article of paranoia because suddenly AI can predict what article to write or image to generate based on troves of data that it can mimic derived from a prompt. It’s a neat trick for sure, and lets our creations come to life much quicker, but can we slow down a bit on how we’ll all lose our jobs in the next five years? Please, don’t take just my word for it though, let’s look at history, and reality, before we get the pitchforks and torches… or just accept our fate without some logic.

The History of Innovation

Throughout history, every major innovation has been met with fear and skepticism. The Ottoman Empire’s approach to the printing press, particularly in the context of Arabic script, is a notable example of how technological adoption can be influenced by cultural, religious, and political factors. The printing press, invented in the 15th century by Johannes Gutenberg in Europe, revolutionized the spread of knowledge, but its reception in the Ottoman Empire was markedly different. One of the primary reasons for the resistance to the printing press in the Ottoman Empire was religious and cultural. The Islamic scholars and calligraphers held significant social and political power, and they viewed the printing press as a threat to their status and the traditional methods of reproducing texts, particularly the Quran. In 1485, Sultan Bayezid II issued a decree that effectively banned the printing of Arabic scripts. This ban lasted for over three centuries, with some exceptions made for non-Muslim communities in the empire. Jews and Christians were allowed to print in their languages (Hebrew, Armenian, Greek, etc.) earlier on, but the printing of Arabic scripts by Muslims remained heavily restricted until the 18th century.

The First Industrial Revolution, which began in the late 18th century, reshaped the world, leading to significant socio-economic shifts. Many feared that machinery would replace human labor, rendering workers obsolete. However, history shows us that while technology did displace some jobs, it also created new ones, increased productivity, led to economic growth and improved living standards overall.

The Second Industrial Revolution, occurring between the late 19th and early 20th centuries, further accelerated technological advancement. It introduced mass production, telecommunications, and transportation innovations such as the railroad and the steamship, connecting the world in unprecedented ways. Like its predecessor, this period faced its share of apprehensions; the rapid urbanization and changes in employment patterns sparked debates and adjustments in societal structures. However, it also led to a surge in economic growth, job creation, and the birth of new industries, illustrating the complex relationship between technological progress and workforce transformation.

Similarly, the introduction of computers and the internet in the 20th century during the Third Industrial Revolution transformed industries and economies. Critics once predicted widespread job losses, yet these technologies led to the creation of entirely new sectors, such as software development, digital marketing, and e-commerce, proving that innovation often opens more doors than it closes.

Understanding AI’s Potential Impact

As we stand on the brink of the AI revolution, it’s essential to approach the discourse with a balanced perspective. AI, like its predecessors, will undoubtedly transform the job market. Some roles will become obsolete, but new ones will emerge in their stead, particularly in AI development, data analysis, and cybersecurity, to mention a few. Further, the rate of advancement will be dictated by the level of adoption by the masses, and when we look at how long it took for us to adopt personal computers as a standard household appliance or business tool, the rate of growth of AI is debatable. The costs and applications will be significant, early adopters will love what it can do, but we’ve seen the amount of interaction with the leading AI tools in the market drop after the initial excitement experienced. This can be credited toward the additional work required even after something has been developed, the tools producing inaccurate information and the lack of consistent everyday use due to it being a new activity we have to remind ourselves to perform.

Moreover, AI has the potential to enhance human capabilities rather than replace them outright. It can automate tedious and repetitive tasks, allowing humans to focus on creative, strategic, and interpersonal activities that machines cannot replicate. This synergy between human intelligence and artificial intelligence could lead to unprecedented levels of productivity and innovation.

Navigating the Future with AI

The key to leveraging AI’s potential benefits while mitigating its risks lies in adaptation and education. As a society, we must prioritize lifelong learning and re-skilling to prepare the workforce for the changing landscape. Certainly governments, educational institutions, and businesses need to collaborate on creating pathways for individuals to transition into emerging fields, but even more, humans need to adopt a growth mindset and be willing to do the extra work.

Furthermore, ethical considerations must be at the forefront of AI development and is a top concern from a regulatory standpoint. Ensuring that AI is used to enhance the human experience rather than diminish it requires careful regulation, education and oversight. By setting clear guidelines and fostering an environment of responsible innovation, we can harness the power of AI to solve complex problems and improve the quality of life for all.


The narrative that AI will unequivocally take your job is an oversimplification of a much more complex and dynamic reality. History teaches us that innovation can be a tide that lifts all boats, provided we navigate it with foresight and inclusivity. Instead of succumbing to fear-mongering, let’s embrace the opportunities AI presents to create a future where technology and humanity advance hand in hand.

As we continue to explore the uncharted territories of AI, it’s crucial to remember that we are the architects of this future. By fostering a culture of innovation, ethical responsibility, and lifelong learning, we can ensure that AI becomes a tool for empowerment rather than a source of displacement.

The Easy Path to AI

The explosion of interest in AI due to the recent success of ChatGPT, the state-of-the-art natural language generation model that can write anything from essays to poems to code, is no surprise. However, now we are starting to see the excitement wane as ChatGPT usage numbers drop. This could be due to competition, concerns about privacy and security, or the overall excitement factor slowing down as users struggle to find uses of the tool. Further, to use the API available from OpenAI, you need a lot of technical skills and resources to train, fine-tune, and deploy it. You also need to be careful about the quality and safety of the generated text, as it might contain errors, biases, or harmful content.

The good news? This is just one tool in a sea of many other AI tools that are refined and purpose-built for many organizational needs. At the top of that list of tools is Microsoft’s Azure Cognitive Services tools, a collection of cloud-based APIs that provide ready-made AI solutions for various scenarios. Anyone who is familiar with Data Science and Machine Learning knows that we need troves of clean and trustworthy data to train an ML model to be able to predict results. The beauty of Cognitive Services is that Microsoft has already built these models around many categories, and even won many “human-parity” awards! Below are just a few examples of how Cognitive Services can help you with your AI needs:

• Speech Recognition: This service allows you to convert speech to text in real time or from audio files. You can use it for voice commands, transcription, dictation, captioning, and more. You can also customize it with your own vocabulary and acoustic machine learning models.
• Computer Vision: This service allows you to analyze and understand images and videos. You can use it for face detection, emotion recognition, object detection, optical character recognition, video indexing, and more. You can also create your own custom vision models using a simple interface. I recently created a video with an overview of the service here:
• Text Analytics: This service allows you to extract insights from text data. You can use it for sentiment analysis, key phrase extraction, entity recognition, language detection, and more. Another example would be to use it to analyze healthcare documents and extract clinical information.
• Many more: Cognitive Services offer a wide range of services for different domains and scenarios, such as natural language understanding, conversational AI, anomaly detection, spatial analysis, personalization, and more.

You don’t need to worry about building or managing your own AI models, or if required, many of the services allow for custom models to be built as well. Once determined, you just need to connect to the API and start using it in your applications. Even better, many of the services can be containerized within a docker container where you can deploy the models locally or in other clouds for an even faster prediction for your application. Finally, you also get the benefits of Microsoft’s expertise and innovation in AI, such as high accuracy, reliability, security, and compliance.

To get started, many of the services have free service tiers for minimal transactions, and each service is billed based on consumption, so as long as you are controlling those transactions, you don’t have to worry about cost overruns, etc.

So, what are you waiting for? If you want to add AI capabilities to your applications without the hassle and complexity of ChatGPT and similar tools, Cognitive Services are the way to go! And if you really want to go deeper in understanding all the capabilities check out our recent book “Practical Guide to Azure Cognitive Services” from Packt or through other online book retailers:

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3 Key Differences in ChatGPT and Azure OpenAI

In this vLog I discuss some misconceptions around ChatGPT and Azure OpenAI, to include:

  • Who owns ChatGPT, OpenAI, and how Microsoft got involved
  • Security and privacy concerns about Azure OpenAI and ChatGPT
  • How each of the services is consumed and billed

Take a look to find out more!

 ChatGPTAzure OpenAI
OwnershipOwned by OpenAI LP a for profit arm of OpenAI the non-profit who’s mission is to development of societyPart of Azure AI offerings as APIs, and investor in OpenAI for exclusive rights to technology generated
SecurityInsecure and open to the public. LLM is trained on dataset created in GPT 3 and currently only references data from 2021 and earlier. Questions and interactions are captured and can be used for further trainingSecure to an Azure tenant using GPT-4, GPT 3.5, Codex and Dall-e Requires access for tenant to reduce the chances of the AI to be used for malicious purposes Data is not stored by the prompt, and model is not trained on data added
CostsFree during preview stages, or paid for better hardware availabilityBased on a consumption model like other Azure Cognitive Services. Biggest expense is re-training the model you’ve deployed for your data

Azure Cognitive Services

AI solutions are exploding, and Azure has the most complete offering of any cloud provider! Watch this video to get started with our API based Cognitive Services in Azure and a sample architecture of how to employ them with the Azure Bot Service. Azure Cognitive Services are cloud-based services with REST APIs and client library SDKs available to help you build cognitive intelligence into your applications.

You can add cognitive features to your applications without having artificial intelligence (AI) or data science skills. Azure Cognitive Services comprise various AI services that enable you to build cognitive solutions that can see, hear, speak, understand, and even make decisions. Azure Bot Service enables you to build intelligent, enterprise-grade bots with ownership and control of your data. Begin with a simple Q&A bot or build a sophisticated virtual assistant.

#Azure #AI #CognitiveServices #ArtificialIntelligence #Bots #ReferenceArchitecture #MachineLearning #API #Cloud #Data #DataScience

Microsoft Reimagines Traditional SIEMs with Azure Sentinel

If you’re like most, security is at the forefront of your mind for your organization. You need the right tools and the right team to keep up with the balance of increasing number of sophisticated threats and with security teams being inundated with requests and alerts.

Today I’d like to tell you about Microsoft’s reimagined SIEM tool Azure Sentinel. Over the past 10 to 15 years, Security Information and Event Management (SIEM) has become extremely popular as an aggregation solution for security and events that happen in our network.

There are also software tools, hardware appliances and managed service providers that can help support your corporate needs to better understand the level of risks in real-time and over a span of time. They do things such as log aggregation, event correlation and forensic analysis and offer features for alerting, dashboarding and compliance checks.

These are great resources to help secure our environment, our users and devices. But unfortunately, the reality is security teams are being inundated with requests and alerts. Compile this with the noteworthy shortage of security professionals in the world – an estimated 3.5 million unfilled security jobs by 2021 – this is a major concern.

Microsoft decided to take a different approach with Azure Sentinel. Azure Sentinel provides intelligent security analytics at cloud scale for your entire enterprise. It makes it easy to collect data across your entire hybrid organization on any cloud, from devices to users to applications to servers. Azure Sentinel uses the power of AI to ensure you’re quickly identifying real threats.

With this tool:

  • You’ll eliminate the burden of traditional SIEMs as you’re eliminating the need to spend time on setting up, maintaining and having to scale the infrastructure to support other SIEMs.
  • Since it’s built on Azure, it offers virtually limitless cloud scale while addressing all your security needs.

Now let’s talk cost. Traditional SIEMs have proven to be expensive to own and operate, often requiring you to commit upfront and incur high cost for infrastructure maintenance and data ingestion. With Sentinel, you pay for what you use with no up-front costs. Even better, because of Microsoft’s relationships with so many enterprise vendors (and more partners being added) it easily connects to popular solutions, including Palo Alto networks, F5 networks, Symantec and Checkpoint offerings.

Azure Sentinel integrates with Microsoft Graph Security API, enabling you to import your own threat intelligence feeds and to customize threat detection and alert rules. There are custom dashboards that give you a view to allow you to optimize whatever your specific use case is.

Lastly, if you’d like to try this out for free, Microsoft is allowing you to connect to your Office 365 tenant to do some testing and check it out in greater detail. This product is currently in preview, so there may be some kinks but I’m looking forward to seeing how it develops in the future, as a true enterprise-class security solution for your environment, whether in the cloud, on premises, in data centers or remote users or devices.

Accelerate Your AI with Machine Learning on Azure Data Box Edge

In some past blogs I’ve discussed Azure Data Box and how the Data Box family has expanded. Today I’ll talk about Azure Data Box Edge (in preview) and elaborate on the machine learning service that it provides in your premises with the power of Azure behind it.

If you don’t know, Azure Data Box Edge is a physical hardware device that sits in your environment and collects data from environment sources like IOT data and other sources where you might take advantage of the AI features offered by the device. It then takes the data and sends it to Azure for more processing, storage or reporting purposes.

Microsoft recently announced Azure Machine Learning hardware accelerated models provided by Project Brain Wave on the Data Box Edge. Because most of our data is in real world applications and used at the edge of our networks – like image and videos collected from factories, retail stores or hospitals – it can now be used for things such as manufacturing defect analysis or inventory out of stock detection in diagnostics.

By applying machine learning models to the data on Data Box Edge, it provides lower latency (and savings on bandwidth cost) as we don’t have to send all the data to Azure for analysis. But it still offers that real time insight and speed to action for critical business decisions.

You can enable data scientists to simplify and accelerate the building, training and deployment of machine learning models using the Azure Machine Learning Service which is already generally available. They can access all these capabilities in their favorite Python environment, using the latest open source frameworks such as PyTorch, TensorFlow and sci-kit-learn.

These models can run on CPUs and GPUs, but this preview expands that out to field programmable gate array processes (FPGA), which is the processor on the Data Box Edge.

The preview is currently a bit limited but, in this case, you’re able to enhance the Azure Machine Learning Service by training a TensorFlow model for image classification scenarios. So, you would containerize that model in a docker container and then deploy it to the Data Box Edge IOT hub.

A good use case for this is if you’re using AI models for quality control purposes. Let’s say you know what a finished product should look like and what the quality specs are, and you build a model defining those parameters. Then you take an image of that product as it comes off the assembly line; now you can send those images to the Data Box Edge in your environment and more quickly capture defects.

Now you’re finding the root cause of defects quicker and throwing away fewer defective products and therefore, saving money. I’m looking forward to seeing how enterprises are going to leverage this awesome technology.