8 inventions that will change the world according to the newest technology trends for 20238 inventions that will change the world according to the newest technology trends for 2023:

I published a piece in 2020 called:

“The Next Big Thing” in Technology: 20 innovations that will impact the world. Since then, more than 70,000 people have read it and more than 1,000 people have shared it on social media. However, anyone who reads it today would realise that what was said back then is no longer relevant. It’s time for an update that highlights the important technologies that are currently in development and will be mainstream by 2030 (or 2028… or 2032).

These timelines should only be used as a guide because projections are frequently off, especially in the world of technology (if you don’t believe me, look at my predictions for technology in 2022). This is due to the fact that we frequently utilise history, which is fundamentally the study of surprises, as a roadmap for the future. This should not deter us, though, from trying to predict the future better because knowing what will happen in the future will help us choose the best course of action to take. Even though we don’t have the answer, we may at least stimulate discussion by posing pertinent questions!

1. Industry-scale nuclear fusion:

inventions that will change the world
©inventions that will change the world

The process of energy being released when pieces of hydrogen (or “atomic nuclei,” if you’re fancy) are subjected to intense heat and combine is known as nuclear fusion. The process produces enormous amounts of energy, which mankind needs more and more of. By the way, the sun functions in a similar manner.

The significance of nuclear fusion is due to the nearly limitless amount of water-soluble hydrogen available on earth and the fact that the sole byproduct is the inert gas helium. Due to the lack of nuclear waste produced by fusion reactors, they are also safer than fission reactors. Commercial nuclear fusion power would increase energy availability and security while offering inexpensive utility-scale electricity with minimal negative environmental effects.

Many nations have made significant investments in fusion research, and private businesses are also running their own tests. The first reactor that could produce energy-positive fusion is the ITER reactor, which is being built in France and scheduled to start operating in 2026; however, hundreds of additional reactors are being built as well.

Fusion research, however, moves slowly and requires a lot of money. The major problem with the technique is that to initiate the fusion reaction, rectors now require temperatures higher than those on the sun. The energy required to do so exceeds the energy generated by the reaction. Despite recent developments, it is still unclear when fusion power will be commercially operational, which is probably more than ten years away.

The International Atomic Energy Agency has more information about the use of commercial nuclear fusion energy.

2. Knowledge sharing by robots:

Robots are impressive, but they are also incredibly foolish, whether they are in factories, warehouses, homes, or on the street. They perform effectively in tightly regulated situations, but every new circumstance causes them to become confused or malfunction, which is usually quite costly. That is altered by “robot knowledge sharing” technology. The idea is being advanced by researchers who want to standardise how robots communicate with one another.

Trial and error, also known as reinforcement learning, is a well-known effective method for AIs to learn new things. It could manifest in a variety of ways, from the mere awareness that a roadside obstruction has materialised to the capacity to grasp a challenging shape. Costs will drop rapidly while productivity will increase dramatically.

We won’t be able to put the genie back in the bottle once it is out. But let’s postpone the Terminator discussions till 2040: before the idea is practical, a lot of hardware, sensors, ML techniques, and data setup must be standardised. Since everyone will want to do things the proper way, nothing may get done. In the end, humanity may only be saved by its inability to reach consensus on anything.

The University of Southern California has further information about robot knowledge sharing.


Let me discuss the nature of job in the future. Without doing an interview, you will work on a project you enjoy. You’ll work for many different employers, and each one of them will pay you in accordance with a contract you didn’t sign. With coworkers you don’t know, you will fight for rewards while keeping an eye on everything they do. You and all of your clients will have input on the company’s strategy. You’ll be mobile, interconnected, and always “on.” Sound like a dream to you? You are correct. An nightmare? Exactly again. The world of decentralised autonomous organisations is a warm welcome.

Many DAO supporters believe that in the near future, humans will collaborate in groups to make decisions in the digital realm. Two important tools will assist them in doing this. First, all organisational rules will be stated as a sequence of “IF/THEN” statements that are directly programmed into a blockchain, making them both permanent and auditable. Second, “digital governance tokens” — which are also stored on a blockchain — will be used to distribute voting shares to stakeholders. Theoretically, carrying out tasks in this manner substitutes both the legalese used by modern organisations (rules are codified) and their hierarchical structure (every stakeholder has a say).

Technically, Decentralised Autonomous Organisations can be formed from any type of organisational structure. Companies in the investment, consultancy, engineering, etc. But that’s only a notion; the truth is more nuanced… and fascinating.

4. The digital twin of a client:

Have you ever desired the most uninteresting crystal ball there is? Companies are constructing just that by utilising AI to create digital twins of their customers, so stop wanting more. Only the portion of the future when you determine which brand of toilet paper to purchase will they be able to forecast. Trust your gut if that sounds like a Balzac premise.

It is increasingly simple to make capitalistic digital clones of particular people or identities with appropriate data and dynamic algorithms. This would be done in order to not only comprehend and forecast behaviour but also to determine how adjustments to the purchasing environment will impact client choices made in light of the information available about them. Companies must make sure they choose the best products, services, promotions, and marketing strategies as marketplaces become more competitive and borrowing costs rise. The cost of failures will undoubtedly be lower and stockholder profits will be increased if we can simulate client groups the way we used to with The Sims. I assume that’s the whole goal.

The newest technology is already in use and will undoubtedly benefit specific client profiles or groups. Individuals may experience things differently, though. We may at last be cautious enough about having our data collected for manipulation. Businesses that want to develop Digital Twins must build credibility for their technology and approach. How could they forecast the unpredictable (2020, anyone?) even then?

5.  6G network:

Three years ago, I was writing about 5G and the myriad industries it would reshape, including the Internet of Things, autonomous vehicles, and entertainment. And we’re still doing plenty of testing with it. Yet it’s now time to consider 6G as the internet service of the future. The fact that the characteristics of 6G are still being worked out is good news in my book. In this sense, we might have high expectations for what technology can accomplish and then whine when we’re unavoidably let down. Even though design and development have already started, the commercialization of 6G won’t happen until around 2030, therefore “later” in this scenario might be a bit off. This follows the well-known pattern of the telecom sector, which adds a new generation every ten years.

Here is what we do know for the time being: the technology will outperform 5G in terms of peak data rate, latency, connection density, and energy efficiency. It remains to be seen whether the change will be as big as the transition from 4G to 5G was. The majority of specialists concur that secrecy, security, and privacy, as well as AI, will be crucial elements of the technology.

The government’s involvement in the design of 5G will be wholly unique; the governments of Korea, Japan, and the United States already want to have a role in the nation’s future infrastructure. The privacy impact of that has yet to be determined.

6.4D printing:

Confusion can arise from the term “4D printing”; I don’t want to imply that people will be able to build and enter another dimension. A product that is 4D printed is essentially a 3D-printed object that can change its qualities in response to an external stimuli (such as being submerged in water, heated, shaken, or not stirred…). Smart Materials are consequently the fourth dimension.

Finding the appropriate “smart material” for all applications—currently, a hydrogel or a shape memory polymer—is the main issue for this technique. Although some progress has been made in this area, we are still far from customer-ready because we haven’t mastered the reversible alterations of some materials.

Healthcare (pills that only work when the body temperature reaches a certain level), fashion (clothes that tighten in cold weather or shoes that improve traction in wet conditions), and homemaking (furniture that stiffens in response to a certain stimulus) are some very promising industries where applications are still being discussed. Computational folding is another interesting application whereby larger-than-printer objects can be produced in a single piece.

7.AI for Generative Design:

Deep learning is used in generative AI technology to produce creative materials including films, stories, training data, drawings, and schematics. Even though you may have experimented with (and appreciated!) apps like ChatGPT and Midjourney, these are merely passing distractions.

Enterprise uses for generative AI are far more sophisticated. If used to its full extent, this latest technology can reduce product-development life cycle time, design drugs in months instead of years, compose entirely new materials, generate synthetic data, optimise part design, automate that by 2025, 30% of outbound marketing messages from large organisations will be synthetically generated, and by 2030, a major blockbuster film will be released with 90% of the film generated by AI.

The technology has highly practical applications that can be used right away and will be implemented with increasing success over the next ten years. That is, if we can avoid the numerous dangers posed by generative AI. Deepfakes, copyright concerns, and other difficulties are what I’m most concerned about.

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