8 Expansion Concepts in AI to Look Out for by 2035

There is no reason to not think of the most “Unrealistic” possibilities when you’re considering the topic of Artificial Intelligence. It’s not so much about what we think is impossible, but rather about pushing those boundaries of what we currently perceive as realistic. The rapid advancements in AI over the past decade have already transformed our world in ways we couldn't have imagined before. AI continues to become more of an integral part of our lives daily, and this innovation train has no brakes.

Think about the elites in the world; Elon Musk, Jeff Bezos, Mark Zuckerberg... Each of them had to think of possibilities that pushed the perceived boundaries of technology. This is my point here, AI extends beyond mere convenience, AI has the potential to reshape entire industries and even address complex global challenges which is the premise for what we’re going to look at in this blog.

As we look ahead, here are some potential expansions in AI to look out for by 2035 that may seem ambitious today:

AI-Enabled Climate Modeling: Climate change is one of the most pressing issues of our time, and AI can play a crucial role in understanding how to mitigate its impact. Algorithms could analyze big bundles of climate data, satellite imagery, and historical patterns to create super-accurate climate models. These models could help scientists and policymakers predict climate trends with greater precision thus leading them to make more informed decisions and develop effective strategies for adaptation and mitigation.

AI-Augmented Social Services: Social service systems, such as welfare, healthcare, and community support, could win big with the adoption of AI advancements. For instance, AI algorithms could analyze individual needs, socioeconomic factors, and public health data that would then be used to improve service delivery and resource allocation. For example, think of chatbots and virtual assistants that provide personalized guidance and support to those seeking social services, AI systems could reduce the burden on caseworkers.

AI-Driven Sustainable Infrastructure: With the increase in demand for sustainable infrastructures, AI can optimize planning, construction, and maintenance processes. AI algorithms could analyze things like geospatial data, energy consumption patterns, and transportation flows to design eco-friendly and energy-efficient cities. A great example of this ideal infrastructure is Barcelona, one of the world's first “Smart cities”. We could even get more ambitious and say that AI-powered systems could take over managing traffic, mitigate congestion, and enhance transportation networks altogether, ideally reducing carbon emissions and improving urban livability as we know it. 

Still too realistic? Let’s get bolder…

AI-Assisted Scientific Discoveries: AI has already demonstrated its potential in accelerating scientific research, and by 2035, it could revolutionize the pace of scientific discoveries altogether. AI algorithms could sift through the vast scientific literature, analyze experimental data, and identify patterns that could easily be overlooked. Ultimately, this could lead to breakthroughs in fields such as drug discovery, materials science, and genomics, which would open new frontiers of knowledge and propel scientific advancements to heights we can’t even comprehend right now. 

AI for Inclusive Accessibility: Inclusive design and accessibility are critical considerations for society just as much as they are for the future of AI. By 2035, AI could make significant advancements in assisting individuals with disabilities. For instance, natural language processing (NLP) and computer vision technologies could enable seamless communication and interaction for people with hearing or visual impairments. AI-powered assistive devices could ultimately enhance mobility and independence. It’s ambitious innovations like this that would build a reality where technology truly empowers and includes everyone. 

AI for Mental Health and Well-being: Mental health is a growing concern worldwide, and AI has the potential to revolutionize mental health care. By 2035, AI algorithms could perform tasks like analyzing behavioural patterns, sentiment analysis, and physiological data that could all provide personalized mental health assessments and interventions. AI-powered chatbots and virtual therapists for example could offer 24/7 support, provide coping strategies, and identify early warning signs.

Personal Finance: When it comes to FinTech, there’s a lot that could happen with the rapid development of AI. Think about things like AI-powered financial advisors who could provide personalized advice and optimize investment portfolios, while fraud detection algorithms could enhance security. Automated budgeting tools would simplify expense tracking and offer insights for better financial management. AI could also improve credit scoring, leading to fairer loan approvals. And lastly, personalized financial education platforms could mitigate the knowledge gap and set people up for financial success.

AI-Powered Cybersecurity: As cyber threats become more complex, AI is poised to step up and beef up our cybersecurity defences by 2035. Imagine AI algorithms keeping a constant watch on network traffic, spotting suspicious patterns that could signal cyber attacks, and swiftly taking action to block them. Machine learning algorithms would be super sleuths, sifting through loads of data to sniff out emerging threats and catch any sneaky anomalies. AI could even develop security features like fingerprint scanners and user behaviour analysis to protect digital systems. 

What’s Next?

By embracing the potential of AI and pushing the boundaries of what we perceive as realistic, anyone can have the opportunity to create an ideal future using technology. Businesses shouldn’t be afraid to let their imaginations run free with this stuff - because the technology to turn a vision into reality could be right around the corner.

With that in mind, here are 3 actionable recommendations for companies to practice over the next 12 years:

1. Nurture an Innovative Culture: Create a work environment that fosters creativity, encourages taking risks, and promotes collaboration. Encourage all employees, regardless of rank, to share their ideas and perspectives. Provide them with the necessary resources and support to explore innovative solutions. Emphasize the value of continuous learning and professional development to keep up with the ever-evolving technologies and market trends.

2. Study your industry’s emerging tech: Stay up-to-date with the latest advancements and closely assess their potential impact on your industry. Invest in R&D to look at how emerging tech like artificial intelligence, blockchain, augmented reality, or even quantum computing could be integrated into your company's operations, products, or services. Encourage the implementation of pilot projects and experimentation to test the feasibility and scalability of these technologies in your business.

3. Forge Strategic Partnerships: Look to collaborate with startups, research institutions, or industry experts, to leverage their expertise and gain fresh insights. Seek out strategic alliances or maybe call them “Joint ventures” that align with your company's goals and complement strengths just as much as weaknesses.

Written By Ben Brown

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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.

5 Skills Needed to Work in Tech Today

As each passing day raises new concerns surrounding the implications of AI, there’s a lot of speculation from workers about what it takes to become indispensable. The thing about this is that it’s not a matter of what you do, but how you continue to do it. As someone who provides value to your industry, you need to adjust to its demands and pay attention to what’s required because that’s what’s going to set your efforts apart long-term.

Artificial intelligence is bound to make people feel that they have to be tech-savvy and understand how to leverage these new tools at maximum capacity. In reality, this may be far-fetched, because there are prerequisites and foundational skills that go beyond technical expertise for workers today and it starts with communication and problem-solving. 

The way AI is evolving suggests that it needs guidance from experts, people who can identify the problems and tasks that the system will solve in the first place. With that said, nothing is slowing down the trajectory of AI anytime soon, so with these prerequisites and foundational skills locked down, here are the areas tech workers need to focus on:

Cloud Computing

This is likely the most fundamental tool needed to develop high-performing, scalable platforms and applications, especially when it comes to AI. Imagine you're a project manager building an application for a telecommunications company that monitors network performance and predicts network failures.

Two aspects of cloud computing you’ll want to focus on might include the following:

• Infrastructure as a Service (IaaS): Understand how to provide and manage virtual machines, storage, and networking resources in the cloud. This is going to demand familiarity with provider offerings, such as AWS EC2, Azure Virtual Machines, or Google Compute Engine, and how to configure and scale these resources to meet the application's requirements.

• Platform as a Service (PaaS): You’ll need platform-level services from cloud providers that streamline app development and deployment. This can include services like Azure App Service, AWS Elastic Beanstalk, or Google App Engine since they offer pre-configured environments for deployment without you having to worry about managing the underlying infrastructure.

Machine Learning

This arguably could have been number 1 since it’s what makes AI as versatile and convenient as it is. In 2021, of all the use cases for machine learning, improving the customer experience accounted for 57% of companies worldwide. 

Two key principles of machine learning that workers should gain familiarity with include the following:

• Unsupervised Machine Learning: Unsupervised learning involves training models on unlabeled data to discover patterns or groupings within that data. Clustering algorithms like k-means, hierarchical clustering, or Gaussian mixture models are good options to identify similar data points or clusters. Dimensionality reduction techniques like principal component analysis (PCA) or t-SNE also help to reduce the dimensionality of data (number of dimensions applied) while maintaining and preserving its structure.

• Supervised Learning: Supervised learning is a popular approach we’re seeing with machine learning where models are trained using labelled data (opposite of unsupervised learning). Tech workers will want to understand the concept of input features and target labels, and how algorithms such as linear regression, decision trees, support vector machines (SVM), or neural networks can be applied to learn patterns and make predictions.

Data Science

Data science is interesting because it combines elements of math, statistics, computer science, and domain knowledge as a means to analyze high volumes of data and identify patterns, trends, and relationships that will then be used to make informed decisions and predictions. It's the driver behind data-driven decision making which Bloomberg identifies as “An elusive aspiration for most organizations”. This highlights the untapped potential of data science since it’s clear organizations recognize the potential value of their data but struggle to turn it into actionable insights. 

Two key aspects of data science for workers to know going forward include the following:

• Data mining: Remember those high volumes of data we mentioned? Well, data mining is what’s going to allow workers to identify those patterns, trends, and relationships we mentioned using algorithms and techniques. Properly leveraging data mining is what’s going to remediate that data overload and turn it into actionable insights.

• Data visualization: This practice involves representing data in visual formats such as dashboards, graphs, charts, and maps. The ability to create clear and concise visual representations of data is crucial for workers to communicate findings, drive that data-driven decision-making processes, and foster a culture of data literacy within their organization. Proficiency in this is an indispensable skill…

Deep Learning

Deep learning is a subset of machine learning that trains neural networks to understand things and be able to make decisions and predictions without being directly programmed to do so. A key differentiator between machine learning and deep learning is that deep learning models excel at handling unstructured and high-dimensional data like audio, images, and text. Deep learning is something that’s going to push the envelope when it comes to what machines can achieve which makes it crucial for tech workers to understand how to leverage it in their work.

Here are two key aspects of deep learning for tech workers to focus on:

• Neural Network Architectures: Understanding different types of neural network architectures is essential in deep learning. For instance, convolutional Neural Networks (CNNs) are commonly used for computer vision tasks, Recurrent Neural Networks (RNNs) are great for sequential data analysis, and Generative Adversarial Networks (GANs) are primed for generating new content. As a tech worker, it’s a great idea to study these architectures and be able to recognize what model is best for different tasks. 

• Training and Optimization: Deep learning models require a lot of computational resources and training to achieve high-level performance. Tech workers need to know various optimization techniques such as gradient descent, backpropagation, and regularization methods (Such as L1, L2, and Dropout) to train deep neural networks effectively. Additionally, understanding techniques like transfer learning or pre-trained models might just help leverage existing knowledge and reduce the training time for specific tasks.

Internet of Things (IoT)

IoT technology is reshaping industries across the globe and ultimately changing the way we interact with our surroundings. Above all else, IoT technology gauges where a business's systems are in terms of performance and enables them to leverage data-driven decision-making. 

Two key aspects of IoT for tech workers to become familiar with:

• Connectivity and Integration: IoT revolves around the premise that having various interconnected devices, sensors, and systems can create a network of objects. Workers need to understand the logistics and technology behind IoT connectivity, such as wireless protocols (e.g., Wi-Fi, Bluetooth, Zigbee), network infrastructure (e.g., edge computing, cloud platforms), and data transmission protocols (e.g., MQTT, CoAP). This is effectively going to let you design, implement, and manage IoT solutions, which ultimately leads to seamless communication and interoperability between the different components.

• Industry-specific Knowledge: You need to understand how to tailor IoT solutions to the specific needs of your sector. For example, healthcare workers might use IoT applications in remote patient monitoring, while manufacturing workers may focus on IoT-enabled predictive maintenance. In essence, it’s not a one size fits all approach, but if you know the industry (Or industries) you’re serving - you can add a lot of value that will be hard to replace. 

The Takeaway

People still have a lot of value to bring to the workforce that compliments the unique potential of artificial intelligence. You have to be willing to try new things and give up old methodologies to move forward. Never fall victim to thinking you know it all, and work like you can never know enough.

Written By Ben Brown

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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.

Redefining Enterprise Architecture: Responding to Tech Demands

When you think about major corporations and their approach to sustaining their business model and brand long-term, how often is information technology the first thing you think about? For most people it’s staying relevant through crafty marketing and branding initiatives, or big partnerships, all of which are important factors but none of which solely can sustain a business today. 

As we know, technology permeates every aspect of business operations. Digital transformation starts with a solid architecture, one that leverages various technological components, such as infrastructure, networks, databases, software applications, and security measures. To be clear, the exact definition for “enterprise architecture” according to Gartner is “a discipline for proactively and holistically leading enterprise responses to disruptive forces by identifying and analyzing the execution of change toward desired business vision and outcomes”. 

Embracing a Proactive Approach

The key takeaway here is that technology is not a reactive measure; it’s something that needs to be a proactive and integral part of an organization's approach to long-term sustainability. The key term is “proactive” because the last thing an enterprise can afford is to, again, be reactive in situations where they’re caught off guard by advancements and disruptions. 

The world lucked out with AI in the sense that there’s been time for companies to explore its potential and experiment with its capabilities. AI offers transformative opportunities for corporations today who can now redefine their business models and align with the demands of the digital era.

How We’ve Always Known Enterprise Architecture (EA)

EAs have been that guidance that creates, integrates, and manages data and technology to align IT capabilities with the business's goals. The focus for enterprises is now on the tech aspect more so than project delivery or strategizing because that stuff is less comprehensive than the role technology plays in the business landscape today. What we’re outlining in this section is the elimination of the need to balance competing priorities and resources within EA.

The Key Technologies and Business Functions That EA Teams Focus on Today

In 2020, a report from Gartner estimated that 60% of organizations in 2023 would rely on EA to lead their approach to digital innovation. While we don’t have exact figures for that to compare against today, we do have an understanding of the technological and business functions that are a focus for EA teams which include:

1. Application Architecture

2. Data recovery

3. Governance, risk, and compliance

4. Cloud management

5. Mobile device management

6. Intelligent automation

7. Cybersecurity 

Think about two manufacturing companies:

One relies heavily on innovation and strategic thinking, so they establish the following:

• Dedicated space for R&D: They allocate a specific area or facility to experiment, prototype, and test concepts before integrating them into the EA.

• Agile methodologies: This could involve methodologies such as Scrum or Kanban, to promote flexibility in their development process. This is what’s going to be key for them to respond quickly to market changes and customer demands.

• Collaborate with other companies: Typically for IT operations, data governance, and business strategy. This is going to leverage expertise and resources that will contribute to innovation and consistently meet objectives. 

• Investments in new technologies: This includes exploring emerging technologies relevant to the industry and leveraging them to enhance their manufacturing processes, product development, and overall operational efficiency.

• Data-driven Decision Making: They prioritize the collection, analysis, and utilization of data in their decision-making processes. This helps them identify opportunities and inefficiencies which further contributes to consistently meeting goals. 

The other company is very project-driven so their focus is on the following: 

• Project management: This company will emphasize strong project management, with dedicated teams and resources for each project. They have well-defined plans, timelines, and milestones to ensure execution is efficient.

• Resource allocation: They prioritize allocating resources based on the specific requirements of each project. This includes assigning personnel, budgeting, and managing project dependencies.

• Stakeholder collaboration: The company emphasizes collaboration and communication with stakeholders, both internal and external, to ensure alignment on project goals, requirements, and expectations.

• Risk management: This company would likely have robust risk management processes in place to identify, assess, and mitigate potential risks and issues that could impact the success of the project.

The whole point of this comparison is for companies to understand the importance of their EA team focusing on one aspect of projects and strategy either on the side of the business or technology. The best EA teams maximize one area before moving to the next and they never skip steps. 

Actionable Recommendations

Closely align your priorities with your business's goals when defining your focus. Gain as much expertise and capabilities as possible in that area, collaborate with stakeholders, and consistently monitor how much you progress. 

You always want to make the value of your EA known to decision-makers by demonstrating how it helps meet objectives. Showcase tangible outcomes and demonstrate the ROI of EA initiatives.

Written By Ben Brown

—

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.