ben brown

October 16, 2023

Business

Sam Altman on The Accidental Success of Artificial Intelligence

ben brown

October 16, 2023

Business

Sam Altman recently came on the Joe Rogan podcast and they had a very casual conversation about the trajectory of AI. This conversation looked at what AI means for the future, the yin and yang of the technology, Sam’s original expectations for his creation (OpenAI), and ultimately what this revolution means in a broad sense.

Sam reflects on some of the biggest innovations in history and outlines similarities to what’s going on with AI. He claims that every evolution in technology throughout history is connected and AI is sort of the end of the rope.

To be honest, this kind of dialogue is very common as the general public’s ideas surrounding AI are still forming. But without a firm grasp on what’s already available - how can we as leaders in tech hold ourselves accountable to help drive the right kind of change?

What Altman outlines in this interview is very interesting, especially when he says that the chain of reactions he expected (10 years ago) for AI taking on jobs followed a ladder like this:

Blue collar labor
Cognitive labour
Creative jobs

He then said that this original prediction ended up being exactly the opposite and that at the moment AI can’t take on entire jobs - but it can help with tasks in people’s jobs to boost productivity.

The Unexpected

Altman’s key insight was that the development and evolution of AI and artificial general intelligence (AGI) may not follow a predetermined or straightforward path. He suggests that the future of AI and AGI may be more gradual and continuous than previously thought, with ongoing refinements, improvements, and challenges along the way.

The biggest takeaway here is how dramatically artificial intelligence has surpassed our expectations. It continues to do so, which raises questions about AGI and how the potential of these systems' self-improving is both exciting and somewhat unpredictable.

As we continue down this path of unexpectedness, one thing that’s critical for businesses and consumers to do is leverage. Leverage every AI tool at your disposal, experiment with them, and spend time with them. The more familiar you become - the better equipped you'll be.

What’s Next?

Artificial Intelligence is very unpredictable as we know, but as a business owner, you have the ability to call the shots necessary to succeed. It starts with understanding opportunities for growth, and being proactive in how you approach them.

That’s why we created the AI Tool - to show businesses where their deficiencies are, and then help them make the right changes. At ISU Corp, our team of AI experts is here to help you succeed long-term!

P.S.

This blog was 99% AI-generated.

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ISU Corp is an award-winning software development company, with over 17 years of experience in multiple industries, providing cost-effective custom software development, technology management, and IT outsourcing.

Our unique owners’ mindset reduces development costs and fast-tracks timelines. We help craft the specifications of your project based on your company's needs, to produce the best ROI. Find out why startups, all the way to Fortune 500 companies like General Electric, Heinz, and many others have trusted us with their projects. Contact us here.

ben brown

September 20, 2023

Business

AI’s Revolutionary Strides in Custom Software Development

ben brown

September 20, 2023

Business

As custom software developers, design thinking is standard practice when it comes to any project. We put the user first and build the solution around their needs… This is nothing new. What’s new is that design thinking has changed and evolved into “platform thinking.”

Platform thinking is the understanding that modern consumers have evolved from passive observers in the product lifecycle to active contributors in the value creation process. For example:

Uber uses platform thinking to connect drivers with people who need rides. The more drivers and riders who use Uber, the more valuable it becomes, because there are more rides available and more people to connect with.
Instagram connects people who want to share pictures and videos. The more people use Instagram, the more valuable it becomes; there are more photos and videos to see and more people to connect with.

Simple enough? Hope so, because now we’re really going to change pace.

Artificial Intelligence Tools in Software Development

AI can assist people in creating and enhancing things, no matter how skilled they are. This approach to platform thinking will become something every business grasps. It’ll eventually get to the point where all employees are also materializing their ideas quickly.

Bear in mind that 41% of all code on GitHub is AI-generated, and as AI becomes an important part of making software, the teams and skills needed will change. AI is not a replacement, so much as it is an extension of work, which in software development always comes back to the team around platform engineering.

The Impact of AI on Software Development Roles

Businesses must anticipate AI's role in platform engineering as they look ahead. With the evolving approach to development, the following are some jobs that will change.

Interaction design roles will surpass UI design roles in demand. As visual AI progresses, the need for a manual UI layout and structuring of business processes will diminish. Interaction designers will guide AI in crafting user interfaces and user experiences through JavaScript design systems, visual guidelines, and consistent user testing.

Business analysts will be dramatically more important in shaping business strategies. AI will likely take on tasks like writing user stories, defining requirements, and even setting acceptance criteria. Instead of just documenting these criteria, BAs will evaluate the AI-generated concepts and align them with the platform-oriented mindset. AI will become the key driver of business strategies, with analysts guiding it in the right direction.

The role of test architecture will be a highly sought-after and well-paid position. With autonomously generated software, continuous testing will be crucial. As the development cycle shortens, the demand for testing will skyrocket. Simply automating user tests based on acceptance criteria won't work anymore.

Test architects will be responsible for designing, implementing, and maintaining intricate test architectures, conducting end-to-end testing of new features, consistently performing exploratory testing, and executing dynamic regression suites that evolve with time.

Software architects will arguably get the most out of AI under this umbrella. Even though we’re still technically in the early stages of AI integration in software development, we are seeing tons of growth in platform engineering. Businesses are shifting away from single-point SaaS solutions and consolidating their efforts on custom-built and SaaS-enabled platforms like ServiceNow, Salesforce, and Workday.

In addition, software architects are devising governance systems to shape code standards, development processes, and other aspects along those lines. Going forward, they will leverage AI to create, enforce, and evolve these systems autonomously.

Putting It All Together

Custom software development is in an interesting spot because even though the industry itself is changing, it too has the power to influence and change other industries. Every facet of a business's operations spiderwebs with AI integration.

In healthcare, maybe it’s automating diagnosis and treatment recommendations. In finance, there could be new approaches to financial planning. Maybe in manufacturing, it’s personless warehouses. These are the kinds of visions we need to dream up as software developers while the change happens in real-time. Clients are looking for partners in transformation which means as a software development company, AI needs to be a priority internally and externally.

The Takeaway

Integrating AI in your business processes starts by knowing when you’re ready and where it’s needed. With that in mind, we created a free tool to help you determine whether or not your business is ready for AI.

How is your industry changing right now? How do you think it will continue to change in the next 5 years? These are the questions you need to be asking yourself in today’s marketplace because there will be stark differences between the companies who do ask and the ones who don’t.

Written By Ben Brown

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ben brown

August 9, 2023

Business

Using AI to Enhance Cloud Computing

ben brown

August 9, 2023

Business

Cloud computing has become an integral part of the modern business landscape since it provides platforms with the ability to scale. As the reliance on cloud infrastructure grows, ensuring its reliability and availability becomes paramount. This is where Artificial Intelligence comes in and helps create these ideal platforms.

The relationship between AI and cloud computing is symbiotic, where AI enhances the infrastructure in terms of what the cloud can do and how reliable it is and cloud computing gives AI the resources and environment it needs to thrive. Here’s a quick example of how this works:

If you woke up one day as the Chief AI Officer at Amazon, and they said to you “We need you to scale our AI capabilities to meet the increasing demand and ensure our cloud infrastructure remains reliable," where would you start?

Commonly, at the root of these limitations in a platform is the level of demand that it encounters which puts major emphasis on areas such as resource management. As a Chief AI Officer, you’d want to first assess what isn’t being optimized, which, if scalability and reliability are in question, means that something is underutilizing the cloud. With that in mind, these are some resources that might go into remediating the issue:

Forecasting demand: Based on Amazon’s user usage patterns, you can predict workloads and have the system allocate resources as needed. When it comes to underutilizing the cloud, it's best to implement an auto-scaling mechanism like AWS Auto Scaling that is meant to ensure the right amount of computing power is given consistently with fluctuations in demand.

Predictive maintenance: The cloud infrastructure is a very complex system with so many different interconnected components and dependencies. For this reason, you’ll want systems that know when issues are going to happen before they do. You can have the algorithms analyze data from sensor readings, server logs, or even performance metrics, the idea is that the system recognizes patterns and can anticipate potential issues.

That’s a glimpse at the reliability side, but now we need to address scalability more in-depth:

Edge computing: As a Chief AI Officer, edge computing is going to stand out as a crucial aspect when addressing scalability. It introduces a paradigm shift in how both data is processed and services are delivered, and it plays a fundamental role in optimizing the cloud's infrastructure. Through edge nodes, AI algorithms will process at the source, minimizing the need for data transmission to centralized cloud servers.

Hybrid and Multi-cloud: When implemented with AI, a hybrid and multi-cloud strategy can be great for distributing workloads, in addition to aiding in what we looked at with predictive maintenance and demand forecasts.

Cloud Computing meets AI and ML

Everything we’ve looked at so far is still scratching the surface in terms of leveraging AI in the cloud. With what we know about the cloud infrastructure, this is a quick look at some tools to watch for:

AI-Driven Auto-scaling: Optimizes resource allocation based on real-time demand patterns.

AI-Enabled Network Optimization: Reduces latency and manages traffic in large-scale cloud environments.

AI-Powered Predictive Analytics: Anticipates workloads and performance issues.

AI-Enhanced Security: Identifies and responds to real-time threats, improving cloud security.

Federated Learning: Allows decentralized machine learning across multiple cloud servers while preserving data privacy.

AI-Driven DevOps: Automates testing, code optimization, and deployment.

Quantum Computing Integration: Uses advanced computational power to quickly solve problems.

Explainable AI: Enhances interpretability of complex AI systems.

AI-Optimized Cost Management: Recommends cost-saving strategies based on cloud usage.

AI-Driven Natural Language Processing (NLP): Improves user experiences with cloud services by understanding natural language queries.

Tech Stacks to Support Integration

You want to leverage AI as effectively as possible in the cloud. To do that, you need a tech stack that prioritizes the following components:

AI frameworks and libraries: Get hands-on with TensorFlow, PyTorch, and Scikit-learn. They offer awesome pre-built algorithms for tasks like image recognition and natural language processing.

Cloud platforms: AWS, Azure, GCP – know your way around them! Get familiar with virtual machines, containers, and serverless computing for scalable AI apps.

Big Data tools: Don't shy away from Apache Spark, Hadoop, or Kafka. These are going to be your best friend for handling massive data sets.

Containerization: Docker and Kubernetes are your pals. Use them to package and deploy AI models.

Edge computing infrastructure: We mentioned it before but make sure to design edge nodes for local data processing and real-time responsiveness.

Hybrid and multi-cloud management: Learn how to balance workloads across different clouds and on-premises infrastructure.

Security and compliance tools: Stay on top of encryption, access controls, and monitoring to safeguard data.

Data storage solutions: Amazon S3, Google Cloud Storage, and Azure Blob Storage are your data allies.

Real-time data processing: Master Apache Kafka or AWS Kinesis for streaming data handling.

Monitoring and analytics: Set up Prometheus, Grafana, or CloudWatch to keep an eye on AI model performance and resource usage.

Moving Forward With AI in The Cloud

AI-driven decision-making is the future of the cloud landscape. As we've explored, the synergy between AI and cloud computing is the cornerstone of next-gen platforms. To scale AI effectively in the cloud, you’ve got to be able to navigate a dynamic landscape that merges the potential of AI with the scalability of cloud computing. This is where experts come in and help, often saving you time and money that would be far better spent on scaling the solution than just trying to figure it out.

If you think this is something your company could benefit from, and you’d like to learn less about the why and more about the how, reach out to us so we can get you started on the right foot.

Written By Ben Brown

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