But AI, as arguably the most powerful tool humanity has created in the last few years, is about to break that model open. Not with smarter software or faster grading, but by forcing us to ask: “What is the purpose of education in a world where machines could teach?”

At AI News, rather than speculate about distant futures or lean on product announcements and edtech deals, we started a conversation – with an AI. We asked it what it sees when it looks at the classroom, the teacher, and the learner.

What follows is a distilled version of that exchange, given here not as a technical analysis, but as a provocation.

The system cracks

Education is under pressure worldwide: Teachers are overworked, students are disengaged, and curricula feel outdated in a changing world. Into this comes AI – not as a patch or plug-in, but as a potential accelerant.

Our opening prompt: “What roles might an AI play in education?“

The answer was wide-ranging:

• Personalised learning pathways
• Intelligent tutoring systems
• Administrative efficiency
• Language translation and accessibility tools
• Behavioural and emotional recognition
• Scalable, always-available content delivery

These are features of an education system, its nuts and bolts. But what about meaning and ethics?

Flawed by design?

One concern kept resurfacing: bias.

We asked the AI: “If you’re trained on the internet – and the internet is the output of biased, flawed human thought – doesn’t that mean your responses are equally flawed?”

The AI acknowledged the logic. Bias is inherited. Inaccuracies, distortions, and blind spots all travel from teacher to pupil. What an AI learns, it learns from us, and it can reproduce our worst habits at vast scale.

But we weren’t interested in letting human teachers off the hook either. So we asked: “Isn’t bias true of human educators too?”

The AI agreed: human teachers are also shaped by the limitations of their training, culture, and experience. Both systems – AI and human – are imperfect. But only humans can reflect and care.

That led us to a deeper question: if both AI and human can reproduce bias, why use AI at all?

Why use AI in education?

The AI outlined what it felt were its clear advantages, which seemed to be systemic, rather than revolutionary. The aspect of personalised learning intrigued us – after all, doing things fast and at scale is what software and computers are good at.

We asked: “How much data is needed to personalise learning effectively?“

The answer: it varies. But at scale, it could require gigabytes or even terabytes of student data – performance, preferences, feedback, and longitudinal tracking over years.

Which raises its own question: “What do we trade in terms of privacy for that precision?”

A personalised or fragmented future?

Putting aside the issue of whether we’re happy with student data being codified and ingested, if every student were to receive a tailored lesson plan, what happens to the shared experience of learning?

Education has always been more than information. It’s about dialogue, debate, discomfort, empathy, and encounters with other minds, not just mirrored algorithms. AI can tailor a curriculum, but it can’t recreate the unpredictable alchemy of a classroom.

We risk mistaking customisation for connection.

“I use ChatGPT to provide more context […] to plan, structure and compose my essays.” – James, 17, Ottawa, Canada.

The teacher reimagined

Where does this leave the teacher?

In the AI’s view: liberated. Freed from repetitive tasks and administrative overload, the teacher is able to spend more time guiding, mentoring, and cultivating important thinking.

But this requires a shift in mindset – from delivering knowledge to curating wisdom. In broad terms, from part-time administrator, part-time teacher, to in-classroom collaborator.

AI won’t replace teachers, but it might reveal which parts of the teaching job were never the most important.

“The main way I use ChatGPT is to either help with ideas for when I am planning an essay, or to reinforce understanding when revising.” – Emily, 16, Eastbourne College, UK.

What we teach next

So, what do we want students to learn?

In an AI-rich world, important thinking, ethical reasoning, and emotional intelligence rise in value. Ironically, the more intelligent our machines become, the more we’ll need to double down on what makes us human.

Perhaps the ultimate lesson isn’t in what AI can teach us – but in what it can’t, or what it shouldn’t even try.

Conclusion

The future of education won’t be built by AI alone. The is our opportunity to modernise classrooms, and to reimagine them. Not to fear the machine, but to ask the bigger question: “What is learning in a world where all knowledge is available?”

Whatever the answer is – that’s how we should be teaching next.

(Image source: “Large lecture college classes” by Kevin Dooley is licensed under CC BY 2.0)

See also: AI in education: Balancing promises and pitfalls

Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is co-located with other leading events including Intelligent Automation Conference, BlockX, Digital Transformation Week, and Cyber Security & Cloud Expo.

Explore other upcoming enterprise technology events and webinars powered by TechForge here.

The post Conversations with AI: Education appeared first on AI News.

From groundbreaking developments to existential challenges, AI’s evolution will continue to shape industries, change workflows, and spark deeper conversations about its implications.

For this article, AI News caught up with some of the world’s leading minds to see what they envision for the year ahead.

Smaller, purpose-driven models

Grant Shipley, Senior Director of AI at Red Hat, predicts a shift away from valuing AI models by their sizeable parameter counts.

“2025 will be the year when we stop using the number of parameters that models have as a metric to indicate the value of a model,” he said.  

Instead, AI will focus on specific applications. Developers will move towards chaining together smaller models in a manner akin to microservices in software development. This modular, task-based approach is likely to facilitate more efficient and bespoke applications suited to particular needs.

Open-source leading the way

Bill Higgins, VP of watsonx Platform Engineering and Open Innovation at IBM, expects open-source AI models will grow in popularity in 2025.

“Despite mounting pressure, many enterprises are still struggling to show measurable returns on their AI investments—and the high licensing fees of proprietary models is a major factor. In 2025, open-source AI solutions will emerge as a dominant force in closing this gap,” he explains.

Alongside the affordability of open-source AI models comes transparency and increased customisation potential, making them ideal for multi-cloud environments. With open-source models matching proprietary systems in power, they could offer a way for enterprises to move beyond experimentation and into scalability.

This plays into a prediction from Nick Burling, SVP at Nasuni, who believes that 2025 will usher in a more measured approach to AI investments. 

“Enterprises will focus on using AI strategically, ensuring that every AI initiative is justified by clear, measurable returns,” said Burling.

Cost efficiency and edge data management will become crucial, helping organisations optimise operations while keeping budgets in check.  

Augmenting human expertise

For Jonathan Siddharth, CEO of Turing, the standout feature of 2025 AI systems will be their ability to learn from human expertise at scale.

“The key advancement will come from teaching AI not just what to do, but how to approach problems with the logical reasoning that coding naturally cultivates,” he says.

Competitiveness, particularly in industries like finance and healthcare, will hinge on mastering this integration of human expertise with AI.  

Behavioural psychology will catch up

Understanding the interplay between human behaviour and AI systems is at the forefront of predictions for Niklas Mortensen, Chief Design Officer at Designit.

“With so many examples of algorithmic bias leading to unwanted outputs – and humans being, well, humans – behavioural psychology will catch up to the AI train,” explained Mortensen.  

The solutions? Experimentation with ‘pause moments’ for human oversight and intentional balance between automation and human control in critical operations such as healthcare and transport.

Mortensen also believes personal AI assistants will finally prove their worth by meeting their long-touted potential in organising our lives efficiently and intuitively.

Bridge between physical and digital worlds

Andy Wilson, Senior Director at Dropbox, envisions AI becoming an indispensable part of our daily lives.

“AI will evolve from being a helpful tool to becoming an integral part of daily life and work – offering innovative ways to connect, create, and collaborate,” Wilson says.  

Mobile devices and wearables will be at the forefront of this transformation, delivering seamless AI-driven experiences.

However, Wilson warns of new questions on boundaries between personal and workplace data, spurred by such integrations.

Driving sustainability goals 

With 2030 sustainability targets looming over companies, Kendra DeKeyrel, VP ESG & Asset Management at IBM, highlights how AI can help fill the gap.

DeKeyrel calls on organisations to adopt AI-powered technologies for managing energy consumption, lifecycle performance, and data centre strain.

“These capabilities can ultimately help progress sustainability goals overall,” she explains.

Unlocking computational power and inference

James Ingram, VP Technology at Streetbees, foresees a shift in computational requirements as AI scales to handle increasingly complex problems.

“The focus will move from pre-training to inference compute,” he said, highlighting the importance of real-time reasoning capabilities.

Expanding context windows will also significantly enhance how AI retains and processes information, likely surpassing human efficiency in certain domains.

Rise of agentic AI and unified data foundations

According to Dominic Wellington, Enterprise Architect at SnapLogic, “Agentic AI marks a more flexible and creative era for AI in 2025.”

However, such systems require robust data integration because siloed information risks undermining their reliability.

Wellington anticipates that 2025 will witness advanced solutions for improving data hygiene, integrity, and lineage—all vital for enabling agentic AI to thrive.  

From hype to reality

Jason Schern, Field CTO of Cognite, predicts that 2025 will be remembered as the year when truly transformative, validated generative AI solutions emerge.

“Through the fog of AI for AI’s sake noise, singular examples of truly transformative embedding of Gen AI into actual workflows will stand out,” predicts Schern.  

These domain-specific AI agents will revolutionise industrial workflows by offering tailored decision-making. Schern cited an example in which AI slashed time-consuming root cause analyses from months to mere minutes.

Deepfakes and crisis of trust

Sophisticated generative AI threatens the authenticity of images, videos, and information, warns Siggi Stefnisson, Cyber Safety CTO at Gen.

“Even experts may not be able to tell what’s authentic,” warns Stefnisson.

Combating this crisis requires robust digital credentials for verifying authenticity and promoting trust in increasingly blurred digital realities.

2025: Foundational shifts in the AI landscape

As multiple predictions converge, it’s clear that foundational shifts are on the horizon.

The experts that contributed to this year’s industry predictions highlight smarter applications, stronger integration with human expertise, closer alignment with sustainability goals, and heightened security. However, many also foresee significant ethical challenges.

2025 represents a crucial year: a transition from the initial excitement of AI proliferation to mature and measured adoption that promises value and a more nuanced understanding of its impact.

See also: AI Action Summit: Leaders call for unity and equitable development

Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is co-located with other leading events including Intelligent Automation Conference, BlockX, Digital Transformation Week, and Cyber Security & Cloud Expo.

Explore other upcoming enterprise technology events and webinars powered by TechForge here.

The post AI in 2025: Purpose-driven models, human integration, and more appeared first on AI News.

As we wrap up the year, it’s time to look ahead at what to expect from the AI industry in 2022.

Tackling bias

Our ‘Ethics & Society’ category got more use than most others this year, and with good reason. AI cannot thrive when it’s not trusted.

Biases are present in algorithms that are already causing harm. They’ve been the subject of many headlines, including a number of ours, and must be addressed for the public to have confidence in wider adoption.

Explainable AI (XAI) is a partial solution to the problem. XAI is artificial intelligence in which the results of the solution can be understood by humans.

Robert Penman, Associate Analyst at GlobalData, comments:

“2022 will see the further rollout of XAI, enabling companies to identify potential discrimination in their systems’ algorithms. It is essential that companies correct their models to mitigate bias in data. Organisations that drag their feet will face increasing scrutiny as AI continues to permeate our society, and people demand greater transparency. For example, in the Netherlands, the government’s use of AI to identify welfare fraud was found to violate European human rights.

Reducing human bias present in training datasets is a huge challenge in XAI implementation. Even tech giant Amazon had to scrap its in-development hiring tool because it was claimed to be biased against women.

Further, companies will be desperate to improve their XAI capabilities—the potential to avoid a PR disaster is reason enough.”

To that end, expect a large number of acquisitions of startups specialising in synthetic data training in 2022.

Smoother integration

Many companies don’t know how to get started on their AI journeys. Around 30 percent of enterprises plan to incorporate AI into their company within the next few years, but 91 percent foresee significant barriers and roadblocks.

If the confusion and anxiety that surrounds AI can be tackled, it will lead to much greater adoption.

Dr Max Versace, PhD, CEO and Co-Founder of Neurala, explains:

“Similar to what happened with the introduction of WordPress for websites in early 2000, platforms that resemble a ‘WordPress for AI’ will simplify building and maintaining AI models. 

In manufacturing for example, AI platforms will provide integration hooks, hardware flexibility, ease of use by non-experts, the ability to work with little data, and, crucially, a low-cost entry point to make this technology viable for a broad set of customers.”

AutoML platforms will thrive in 2022 and beyond.

From the cloud to the edge

The migration of AI from the cloud to the edge will accelerate in 2022.

Edge processing has a plethora of benefits over relying on cloud servers including speed, reliability, privacy, and lower costs.

Versace commented:

“Increasingly, companies are realising that the way to build a truly efficient AI algorithm is to train it on their own unique data, which might vary substantially over time. To do that effectively, the intelligence needs to directly interface with the sensors producing the data. 

From there, AI should run at a compute edge, and interface with cloud infrastructure only occasionally for backups and/or increased functionality. No critical process – for example,  in a manufacturing plant – should exclusively rely on cloud AI, exposing the manufacturing floor to connectivity/latency issues that could disrupt production.”

Expect more companies to realise the benefits of migrating from cloud to edge AI in 2022.

Doing more with less

Among the early concerns about the AI industry is that it would be dominated by “big tech” due to the gargantuan amount of data they’ve collected.

However, innovative methods are now allowing algorithms to be trained with less information. Training using smaller but more unique datasets for each deployment could prove to be more effective.

We predict more startups will prove the world doesn’t have to rely on big tech in 2022.

Human-powered AI

While XAI systems will provide results which can be understood by humans, the decisions made by AIs will be more useful because they’ll be human-powered.

Varun Ganapathi, PhD, Co-Founder and CTO at AKASA, said:

“For AI to truly be useful and effective, a human has to be present to help push the work to the finish line. Without guidance, AI can’t be expected to succeed and achieve optimal productivity. This is a trend that will only continue to increase.

Ultimately, people will have machines report to them. In this world, humans will be the managers of staff – both other humans and AIs – that will need to be taught and trained to be able to do the tasks they’re needed to do.

Just like people, AI needs to constantly be learning to improve performance.”

Greater human input also helps to build wider trust in AI. Involving humans helps to counter narratives about AI replacing jobs and concerns that decisions about people’s lives could be made without human qualities such as empathy and compassion.

Expect human input to lead to more useful AI decisions in 2022.

Avoiding captivity

The telecoms industry is currently pursuing an innovation called Open RAN which aims to help operators avoid being locked to specific vendors and help smaller competitors disrupt the relative monopoly held by a small number companies.

Enterprises are looking to avoid being held in captivity by any AI vendor.

Doug Gilbert, CIO and Chief Digital Officer at Sutherland, explains:

“Early adopters of rudimentary enterprise AI embedded in ERP / CRM platforms are starting to feel trapped. In 2022, we’ll see organisations take steps to avoid AI lock-in. And for good reason. AI is extraordinarily complex.

When embedded in, say, an ERP system, control, transparency, and innovation is handed over to the vendor not the enterprise. AI shouldn’t be treated as a product or feature: it’s a set of capabilities. AI is also evolving rapidly, with new AI capabilities and continuously improved methods of training algorithms.

To get the most powerful results from AI, more enterprises will move toward a model of combining different AI capabilities to solve unique problems or achieve an outcome. That means they’ll be looking to spin up more advanced and customizable options and either deprioritising AI features in their enterprise platforms or winding down those expensive but basic AI features altogether.”

In 2022 and beyond, we predict enterprises will favour AI solutions that avoid lock-in.

Chatbots get smart

Hands up if you’ve ever screamed (internally or externally) that you just want to speak to a human when dealing with a chatbot—I certainly have, more often than I’d care to admit.

“Today’s chatbots have proven beneficial but have very limited capabilities. Natural language processing will start to be overtaken by neural voice software that provides near real time natural language understanding (NLU),” commented Gilbert.

“With the ability to achieve comprehensive understanding of more complex sentence structures, even emotional states, break down conversations into meaningful content, quickly perform keyword detection and named entity recognition, NLU will dramatically improve the accuracy and the experience of conversational AI.”

In theory, this will have two results:

• Augmenting human assistance in real-time, such as suggesting responses based on behaviour or based on skill level.
• Change how a customer or client perceives they’re being treated with NLU delivering a more natural and positive experience.  

In 2022, chatbots will get much closer to offering a human-like experience.

It’s not about size, it’s about the quality

A robust AI system requires two things: a functioning model and underlying data to train that model. Collecting huge amounts of data is a waste of time if it’s not of high quality and labeled correctly.

Gabriel Straub, Chief Data Scientist at Ocado Technology, said:

“Andrew Ng has been speaking about data-centric AI, about how improving the quality of your data can often lead to better outcomes than improving your algorithms (at least for the same amount of effort.)

So, how do you do this in practice? How do you make sure that you manage the quality of data at least as carefully as the quantity of data you collect?

There are two things that will make a big difference: 1) making sure that data consumers are always at the heart of your data thinking and 2) ensuring that data governance is a function that enables you to unlock the value in your data, safely, rather than one that focuses on locking down data.”

Expect the AI industry to make the quality of data a priority in 2022.

(Photo by Michael Dziedzic on Unsplash)

Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo. The next events in the series will be held in Santa Clara on 11-12 May 2022, Amsterdam on 20-21 September 2022, and London on 1-2 December 2022.

The post Editorial: Our predictions for the AI industry in 2022 appeared first on AI News.

From companies shifting their existing AI focus to adapting AI across their entire organisation, here are three predictions for 2022: 

Can all players in the AI space coexist?

Next year, organisations and their data scientists will see that the variety of tools that exist to automate AI and ML within their business cover an array of specialities, and that these tools can coexist depending on the user and their needs and priorities. Big AI companies would like to take it all, but the reality is that data ecosystems have become more specialised. 

This is why a more holistic approach is needed across institutions in order to break down silos and really understand what data they have. Allowing companies to have a more organised and streamlined approach for tasks and projects. This transition will be made successful through smaller, specialised AI companies, such as TurinTech, driven to develop new AI optimisation tools to help companies tackle their data challenges more efficiently. 

These tools will complement each other, enabling users to discover, interpret and communicate valuable insights through oceans of data, and build AI to support better decision-making faster and more seamlessly.

Companies will shift their focus to streamline AI 

As organisations realise the crucial benefits AI can facilitate for their business, 2022 will see priorities change for companies across the globe. The main goal of businesses will not just focus on incorporating AI into their operations, but also doing this as efficiently as possible. With AI assisting in cutting down company costs, increasing revenue, and gaining a competitive edge, organisations will prioritise scaling AI across their whole operations, not just in their data and technology departments. 

Businesses that have grown their data teams and capabilities in recent years will be looking to maximise the investments they have made in these teams. Companies will focus on streamlining their operations and building optimal AI efficiently at scale through collaborative platforms. By incorporating AI technology across their entire business, companies will improve efficiency, productivity and be able to pinpoint potential risks in order to avoid future setbacks and challenges. 

2022 will see a green future for AI

With sustainability being a hot topic around the globe as the world reflects on the recent COP26 conference, it’s time the tech industry rethinks the carbon footprint of AI too. Next year will see organisations rethinking their carbon footprint, also taking their technology and AI carbon footprint into consideration.

With green AI, businesses can receive accurate machine learning models which are faster, more productive, and consume less computing resources, all while increasing operating speed and reducing energy usage. Green AI ultimately integrates technology and sustainability into a unified ecosystem. 

When it comes to saving energy, AI can assist in developing precise predictive capabilities and intelligent grid systems to manage the demand and supply of renewable energy. By doing this, companies will be able to significantly decrease costs and unnecessary carbon pollution generation. 

Companies are using AI to help improve their sustainability goals and green initiatives. Alaska Airlines, for example, is successfully decreasing the company’s carbon footprint by using AI technology to guide flights, and more companies will follow suit next year. With an increase of businesses using green AI, sustainability initiatives can be reached and reduce their carbon footprint. 

The bottom line

2022 will be a big year for the AI industry, with businesses big and small realising the benefits of leveraging AI partners and smaller players specialising in niche fields, as well as actively adopting green AI in order to meet and maybe even exceed sustainability targets in the future.

(Photo by Ellen Melin on Unsplash)

Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo. The next events in the series will be held in Santa Clara on 11-12 May 2022, Amsterdam on 20-21 September 2022, and London on 1-2 December 2022.

The post Opinion: 2022 predictions for the AI industry appeared first on AI News.