Let’s say you have a Facebook account.
Often, you log in once a day, in the evenings, slowing scroll through your news feed.
Checking out what your friends are up to. Liking two or three posts, send or accept three friend requests and you are out. Maybe headed to the gym.
Now one day there is a new YOU.
The new YOU logged in at 3 AM in the morning, is quickly scrolling through the news feed like he’s afraid of his own shadow, sending friend requests in mass, and is a little too excited about hitting the thumbs up button.
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Before you realize it, a window pops up. Want to know what’s in there for you?
Your account has been suspended. You have been automatically logged out, and you can’t continue hitting the like button, bothering strangers with friend requests or typing a little too fast in the messenger… like you are pressed for time.
Surprisingly, you can wait and try again in 24 hours and you’ll be just fine then. No human is needed to intervene.
How is this possible?
Well, thanks to artificial intelligence, the machine learning model learnt and adapted your usual behaviour i.e. your Facebook usage habits.
(Both ML and AI are terms that have been searched relatively consistently over the last 12 months, thought machine learning experienced a slight drop from September last year)
So when there was a sudden change in your behavioural patterns, like liking to many posts than usual, aggressive following (not on the streets) of other Facebook users and typing at a speed the algorithm did not expect…
Hint: it’s called spam.
It immediately thinks that your account has been hacked and nips the hacker right in the bud before he can cause damage. Imagine how far AI has come to help fight cybercrime, data breaches and safety online.
In fact, I have another article that dives a little deeper into the impacts of artificial intelligence on cyber security (+examples) if you wanna check that out.
And this is just but a little too common example of AI applications on social medial. There are some terrifying applications, like using a persons face to detect whether they have cancer or not. What?
And we are not done yet.
In the year 2021, you’d be hard-pressed to find a company that has not implemented AI, has outsourced AI development or at least is thinking about it. In fact, analysts recently estimated AI to be a $4 trillion dollar industry.
Yes, you read that right.
But what exactly does 2021 have for us in terms of game-changing AI applications?
Well, in this article, we’ll look at the AI predictions that experts, researchers and those who get their hands dirty with AI & ML tools on a daily basis think will happen in 2021.
Through these artificial intelligence predictions for 2021, you’ll get an idea of what to expect in terms of artificial intelligence, machine learning and their applications in various industries.
And if all this AI stuff really ticks with you, then you’ve go to start learning at least how to train a few AI models to learn from datasets. In fact, I have written another complete guide on how to get started learning AI in 2021. You wanna check out that guide.
Don’t forget that data science, which machine learning and artificial intelligence form part of is one of the most in-demand skills today.
As you can see above, in the last 7 days alone, there were 6,039 data science jobs posted on the LinkedIn jobs board just in California, targeting data scientists of all experience levels.
Now, without much further ado, let’s get into the AI and ML predictions that will define this year.
I received these predictions from a little survey I conducted on tech companies that use AI, AI research institutions and experience AI engineers.
So let’s get to it.
Don’t forget to leave a comment in the comments section about what you think of these predictions for artificial intelligence.
1. Rise of conversational intelligence tools
When meetings happened face to face in the physical world the information everyone had access to in a room was identical. Having an AI assistant in that setting would require things like Google glass.
But with the rise of remote work and most communication moving digital, we’re going to see a rise of conversational intelligence tools that leverage the opportunity to create uneven information scenarios.
A meeting over zoom can have a personalized experience for each individual. We can search, showcase and surface information that might be relevant to just one individual. For example in an online classroom, the instructor and student can have completely different experiences in this world.
Sourabh Bajaj, Tech Lead Google, LLC
2. Increased use of ML techniques in core business offerings
Most ML models are currently deployed to the cloud, which limits their applications. With recent advances in hardware inference engines, it is becoming possible to run machine learning algorithms in constrained compute environments.
Advances in this area will drive exciting new applications for AI, such as AR/VR and autonomous driving.
As advanced ML techniques become more accessible (e.g. with easy-to-use frameworks such as Pytorch) and industry data becomes more widely available, industries will increasingly utilize machine learning techniques in their core business offerings in 2021.
However, I think the industry will adopt these techniques in a more pragmatic method, starting with simpler techniques that are more understandable and less volatile.
Mitchell Spryn, Research Engineer Facebook, Inc
3. Complex business problems will remain complex.
This year more and more people will realize the gap between highly-hyped AI technologies and real business problems.
Although AI, especially deep learning, has been advertised as a technology that can eliminate the need for tedious manual work in any problem, we’ll learn that the reality is not that simple, except for a few specific tasks such as image recognition and NLP.
Complex business problems will remain complex.
General-purpose AI technologies can only solve general problems. However, the process of disillusionment will encourage more down-to-earth discussions on AI empowerment in business data analytics.
Tsuyoshi Ide, AI Research, IBM Corp.
4. More government initiatives on data sharing and availability
AI for defence is hampered by two bottlenecks: accessibility and shareability.
Data is the key enabler to the development of AI, but the defence sector is a data-scarce environment as most data is restricted. Or siloed. Or unstructured.
Moreover, as the data applied to an AI-enabled system needs to be verified and assured, it is not always possible with data derived from civil sectors.
In 2021, I think we will see more initiatives from governments on sharing and availability, as governments start to recognise data as vital.
Robert Limmergard, Secretary General Swedish Security and Defense Industry Association
5. AI in the Supreme Court
In 2021, AI will see it’s seminal moment where the zeitgeist clashes with enterprise adoption for the future of the technology.
On one hand, we will see a high tide for environmental concerns over carbon footprints of transformer models, increased scrutiny of personal data usage, and privacy concerns over computer vision facial recognition that will lead to congressional intervention.
AI may even reach the Supreme Court. And on the other, more AI companies will submit for IPO while large enterprises will report greater earnings and cite AI adoption as a leading factor.
Money will win.
Steve Meier, Co-founder KUNGFU.AI
6. Growth of billion scale datasets
More data generally produces better results in training machine learning models.
The recent success of deep models has led to increasingly large datasets, and hCaptcha serves this need: labelling massive amounts of data in a timely, affordable, and reliable way.
In 2021 we expect datasets used in training models for online services to grow even larger.
Recent work from researchers at Facebook AI and elsewhere has demonstrated impressive accuracy improvements when scaling up to billion-scale datasets, but most problems remain limited by the annotations available.
7. 99% of all AI investments will fail
AI investments will reach record levels in 2021. Just as there are millions of aspiring musicians, only a handful are virtuoso superstars. The same ratio applies to AI technologies and innovations.
My prediction is more than 99% of all Artificial Intelligence investments will fail in 2021 or become a brief one-hit-wonder. Standout successes will happen to those who address a challenge and create buzz with ten-times better breakthrough innovation.
I see a resurgence in digitizing pictures due to several AI photography innovations which animate still photos.
Mitch Goldstone, CEO ScanMyPhotos.com
8. A steady rise in AI-ML investments
Wider awareness and understanding of AI-ML technologies will mean that enterprises will be able to identify areas within their domain that will benefit from AI adoption much more effectively.
This, in turn, will result in significantly augmented value generation and realized benefits from their AI investments, which will fuel a steady rise in AI-ML investments.
Automation will transition from catering to routine, repetitive and predictable tasks towards enabling more expertise oriented activities.
Joydeep Dam, Vice President AI & Innovation Labs at BRIDGEi2i
9. 3D Multi-Sensor Transmitters
Imagine a world in which large items (like a sofa) can be replicated when the original runs its course, by applying technology that can control and synthesize product touch, feel, look, and even smell.
In a virtual, extended reality world, a sofa would look and feel like real leather, only this look and feel will be generated by a 3D multi-sensor transmitter, outfitting a generic base-model sofa with sensors to fuel look and feel, like leather, velvet, or wool, etc. with an added bonus that significantly reduces the use of planet resources.
Some basic building blocks for this already exist today, including military optical cloaking that makes a tank look like a jeep, programmable shock absorbers that make a car drive in different modes, and more.
Zohar Gilad, CEO Fast Simon (formerly InstantSearch+)
10. Government investments in AI research
2021 will not be a time of major technical breakthroughs but will see a significant increase in the AI market and significant resources directed to AI research and development by government and industry.
This new funding will be directed at moving AI from just data-oriented (deep) machine learning to more complicated areas including neuro-symbolic, better language models, and long-term AI planning systems (a real failure of the learning-based systems to date).
Prof James Hendler, Director Rensselaer Institute for Data Exploration and Applications
11. Need for business domain modellers
Knowledge graphs and semantic solutions are supporting explainable AI by providing the model to add context to data.
In parallel, the need for expanded roles and organizational skill sets including business domain modellers or knowledge engineers who can transform both structured and unstructured data to align with how users and customers communicate/capture knowledge are becoming the key enablers for enterprise AI.
Lulit Tesfaye, AI Practice Leader Enterprise Knowledge, LLC
12. AI/ML will lose steam as buzzwords
The future of AI and machine learning is that these terms will roll into the term computing.
They will continue to lose steam as buzzwords as it’s almost expected that AI and machine learning are considered with both external and internal facing software.
There will of course be improvements across all disciplines of AI mostly related to ease of use. Think: your grandmother will be able to use some basic AI.
In terms of NLP and ASR, the space that Sonix plays in, we expect continued improvements.
While we can achieve 100% accuracy on some files today, that is not the case for all audio. In the near term, speaker diarization will improve and accuracy with difficult audio (crosstalk, background noise, and strong accents) will get much better.
Jamie Sutherland, Co-Founder & CEO Sonix AI
13. Deep Fakes will continue to plague the media
In 2021 we will see more democratisation of AI through further releases of large pre-trained models in the Open Source community for example a big update from Open AI on GPT. This is already having a profound effect in NLP, and we’ll see that sophistication spread into end-user applications such as Chatbots.
Deep Fakes will continue to plague our media, but we can expect to see improvements in the detection and filtering of this fake content in a zero-sum game for Computer Vision models.
Finally, we are already seeing an explosion in healthcare, medical and life sciences adopting AI technologies, and due to the lengthy development and clinical trials required in these fields may see some interesting breakthroughs towards the end of 2021 and going into 2022.
Hopefully, there will also be some further regulation around ethics in AI, as we know the FDA is reviewing and introducing Good Machine Learning Practices (GMLP) which will set the foundations for stricter and much-needed regulation for AI creators.
Jack Hampson, CEO DeeperInsights.com
14. Incremental improvements to video modelling
I don’t see “artificial intelligence” changing much in 2021.
I believe the biggest changes will continue to happen in the industry, where carefully designed machine learning models will help automate human tasks (e.g. X-ray readers, insurance models, loan applications, etc.).
We’ll also probably see more incremental improvements to video modelling (e.g. identifying someone shoplifting or when a penalty occurs in football, soccer, basketball, etc.).
However, real “artificial intelligence” doesn’t exist today and I don’t see that changing in 2021.
With regards to machine learning, Neural Networks are the dominant model today, and they’ve been around since the 50s. They’ve improved tremendously since their inception, but I’m still waiting for the day someone introduces a fundamentally new model that gets us closer to the human brain. I’ll be happily surprised to see it in 2021.
Ben Gorman, Founder GormAnalysis
15. Demand for sustainability amongst policymakers
We will start to see an even greater convergence and focus on the nexus between sustainability, measurement, data and artificial intelligence.
This is being driven by greater awareness and demand for sustainability amongst investors and policymakers as well as financial institutions themselves.
To achieve the kind of alignment needed to meet global commitments like the Paris Agreement on climate, there needs to be standardisation of metrics to encourage comparable, useful data.
In the future those that can combine skills and knowledge in big data, AI as well as the business, financial and scientific aspects of this issue will be very useful – this is an area where we need to start upskilling younger workers and graduates.
Matthew Chan, Head of Policy and Regulatory Affairs ASIFMA
16. Embedded ML for on-device analytics
With 250 billion microcontrollers in the world today, embedded machine learning will become the default technology for performing on-device data analytics for vision, audio, motion, and more.
In the near future, we will see this driving significant innovation from accelerating scientific discoveries to consumer devices, medical research, robotics, fully autonomous vehicles, voice-activated assistants to smart manufacturing.
For the first time, embedded machine learning will give billions of devices the brains needed to make smart decisions without needing to send data to the cloud, as it will be small enough to fit into any environment under the most constrained conditions.
Adam Benzion, CEO Edge Impulse
17. Edge computing deployments to accelerate
Building off the pandemic digital momentum, the Neal Analytics team expects to see vision, AI, and edge computing deployments accelerate in 2021.
Computer vision is well-positioned to be the new frontier, taking advantage of edge hardware developments to move closer to the point of data collection and offload AI workloads. This will open new scenarios, especially in retail and healthcare.
In manufacturing, Deep Reinforcement Learning will gain traction with more applications in production yield and supply chain optimization. Data fusion, AI operationalization, and customer order fulfilment will also become focus points across industries.
AI Team, Neal Analytics
18. AI will reinforce family memories
At FamilyAlbum, we use artificial intelligence and machine learning to make it easier for families to look back on special moments.
Our app analyzes uploaded media to compile touching compilation videos, recommend custom photo book and print layouts, sort photos and videos by child, and more.
In 2021, we’ll be focusing on perfecting the features we already have in place, while also developing new features to make it even easier to look back on family memories.
19. Rise of embedded AI apps
In 2021, AI will gain more momentum and an increase in industrial utilization. The IT industry seems to lower the entry barrier for new startups by combining AI with data science.
The steps towards targetting embedded AI apps will provide a convivial environment for blending machine IQ with Human creativity and intelligence.
AI is moving toward complementing the existing infrastructure instead of embedding more complexity. Every industrial tool, if modified a little bit to learn and experience, can tremendously wave off the operational complexity.
Low code development has given machine learning a secret entry to flexible and efficient software, and it is not going to end soon.
Tarun Nagar, CEO Dev Technosys
20. The year when AI scales to domain experts.
There has been amazing progress in AI in recent years.
Data and AI are transforming industries as seen in products provided by the technology giants with large research groups. We all start to expect a similar level of functionality in any and every service we use.
With the progress we see in AI-tooling, such as the Peltarion Platform, we predict that 2021 will be the year when AI scales to domain experts, reducing the need for large research groups.
This will make AI available to a large number of companies in a variety of sectors such as health care, finance, manufacturing, retail, and more.
Anders Arpteg, Head of Research Peltarion
21. AI to predict network performance
We anticipate that AI will play a big role in helping engineers understand, predict, and improve network performance.
At Energy Sciences Network (ESnet), our researchers are developing algorithms to study network patterns and identify anomalies; we’re building automated tools to study TCP traces, which helps us understand how our network behaves and develop new protocols for improving big file transfers over a WAN.
We are also using network automation tools to create intelligent dashboards that allow AI algorithms to make informed decisions by consuming multiple network statistics at once.
The prototype NetPredict Map uses advanced machine learning techniques to predict congestion points in the networks between twenty-four hours to seven days ahead of current network traffic statistics, improving real-time congestion prediction to 85% accuracy.
In addition to that, we’re also developing novel reinforcement learning approaches that could interface with network controllers to help reroute data flows to alternative paths if certain paths are busy or underperforming.
Experts at Energy Sciences Network (ESnet)
22. AI-based pandemic outbreak prediction
It would definitely not be a surprise to see AI/ML’s applications completely focusing on making lives easier during the pandemic and helping with recovery post the pandemic.
While the core Medical Industry definitely would focus on the detection of corona like viruses through evaluation of Chest CT scans, we would see an increase in AI-based pandemic outbreak rate prediction and AI-based decision making for focusing vaccination drives.
We would also see an increase in NLP based pandemic related rumours detectors, the same techniques being used to detect future outbreak waves as well.
In the Skills Assessment Industry, we would see heavy investment in AI-Based Proctoring with Analysis like we are focusing at iMocha. Innovations in auto evaluated assessments and interviews is something that would be on the rise.
Other Industries like the Travel and Tourism Industry would see a rise in AI-Based Vacation Planning to make the most out of newly opened lockdown free off-beat locations, hotels and airlines giving heavy discounts to lure customers and other pandemic based impacts in favour of the travellers.
Vishal Madan, Head of Engineering iMocha
23. AI will become the lifeblood of service operations
Thanks to the COVID-19 pandemic, 2020 saw a decade of digital transformation in the span of a few months. And this pace of change will continue through 2021 and beyond.
In order to keep up with exponential growth in digital interactions, it will become necessary for brands to adopt AI.
At Kustomer, we believe AI will penetrate every aspect of the customer experience, from delivering instant responses to frequently asked questions to routing conversations based on intent.
Simply put, AI will become the lifeblood of service operations in 2021.
It will transform customer service from a post-sales cost-centre into a full lifecycle growth-driver. To be successful, however, organizations need to fuel their AI and machine learning models with CRM data to truly see its value.
Shantala Balagopal, Product Marketing Director Kustomer
24. Use of AI tools to make workforce decisions
The global pandemic has forced many businesses to move to remote work and more employers have been accelerating their use of AI-enabled tools to better manage their workforces.
In 2021, companies are likely going to increase how often they use AI systems for workforce decisions and the types of decisions they use AI for.
For example, companies are already using AI to better recruit and hire new employees, but they are likely going to start using AI tools to help assess employee engagement, personalize development pathways for them, and evaluate employee compensation.
Policymakers will also be increasingly thinking about how to ensure these systems are making responsible decisions.
Hodan Omaar, Policy Analyst Center for Data Innovation
25. More AI technologies will likely get approved by the FDA
In the healthcare space, a number of additional technologies for AI in medicine will likely get approved by the FDA. Some of these technologies will likely also be able to obtain dedicated CPT/reimbursement codes.
AI in medicine will likely start to penetrate areas besides oncology, including diabetes, Alzheimer’s disease and cardiovascular disease.
2021 will also likely see more innovation in non-black-box (interpretable AI) technologies in order to make them more amenable for use by clinicians and physicians.
More innovation will continue in the use of AI not just for diagnosis of disease, but also for predicting patient outcome and predicting and monitoring therapeutic response to specific drugs like chemotherapy and checkpoint inhibitors.
Anant Madabhushi, PhD Professor of Biomedical Eng. Case Western Reserve University
26. Increase in acceptance of AI apps
During COVID-19, businesses and consumers had to adapt to a “new normal,” and as daily interactions with AI increased, there became a greater acceptance from both.
Areas that had once seemed off-limits or limited, like telehealth, saw exponential growth and quickly became the norm.
In 2021, we can expect to see an increase in businesses’ breadth and depth using AI to make consumer’s lives more convenient while driving workplace efficiency.
27. Improved human-machine interactions
AI will be deployed with a huge impact in various industries in 2021.
There will be more and more applications where AI systems combine symbolic AI (e.g. Knowledge Graphs) with machine learning (e.g. deep learning) to incooperate context and meaning to be able to explain the results and internal processes and improve the overall accuracy.
We foresee that in 2021 AI applications will especially focus on improving human-machine interactions (e.g. chat or voice systems or human-friendly robotics) to further assist people in a more natural way in their decision makings, information search, or task automation.
Jürgen Umbrich, Senior Knowledge Scientist Onlim
28. Rise of defensive AI
Security is still top of mind for most in the infrastructure space.
As a consequence, it’s likely we’ll see applications of AI in the datacenter space not just as a tool to help humans perform Root Cause Analysis (such as the new AWS Detective service) or anomaly detection (for log files, or monitoring data) but more and more autonomous behaviour where the infrastructure actively defends itself (defensive AI).
This is more and more a requirement for everybody not just big companies because the rising use of “offensive AI” lowers the barrier of entry for bad guys and everybody is fair game today.
Alex Bordei, VP of Product Management and Engineering MetalSoft
29. Increased adoption of BI tools
In 2021, I expect more businesses with 50+ employees to desire business intelligence tools that handle budgeting and fixed asset management. The use of AI and ML in these applications help turn databases into useful intel that you can use immediately.
One example is a company that operates a number of stores — they can process a high volume of transaction records.
Another example is its use in dashboards, which are primarily used to help companies make decisions. Data can automatically be extracted from Salesforce, Square, Facebook, Shopify, and more–giving insight on inventory, sales, and customers.
Russ Davidson, Digital Marketing Specialist SoftwareConnect.com
30. Use of AI for document management
In 2021, we expect to see AI and machine learning further melded with technology.
With people working digitally and now remotely, we can expect to see more applications of machine learning and AI being integrated into everyday software, like Zoom, email platforms, or desktop document management tools.
For instance, it can be used to better anticipate end-user behaviour, refine pattern recognition for email marketing campaigns, and automate smarter, more efficient document processing tasks. The possibilities are endless.
One certain thing is that we will see AI continue to play a role in developing the digital lifestyles and work environments that are merging due to the pandemic.
Reena Cruz, Brand Manager InvestinTech.com
31. Data annotation services & expertise determine success
The number one reason why AI/ML initiatives fail is due to poor data quality. It’s not the lack of data volume, but data quality.
The second reason is the lack of experience and expertise in producing the right data. Not all data is good and large volumes don’t necessarily help. The market will shift from an ML model-centric approach to a data-centric approach.
Organizations will learn they can achieve more with smaller data sets as long as they:
- establish the right data annotation requirements,
- have the complex workflows to produce it, and
- source skilled annotators to capture it and tools to scale it.
Data will become the new code for successful ML.
Brett Hallinan, Director of Solutions Marketing iMerit.net
32. Rise of contextual intelligence technology
Artificial intelligence and machine learning are playing a big role in the advertising industry now more than ever.
As data privacy regulations continue to grow and personal identity trackers like the cookie are being sunsetted, advertisers are turning to contextual intelligence technology, like the one GumGum offers.
Contextual can be greatly improved by using AI, machine learning, and computer vision to deliver engaging and effective ads in relevant and safe environments.
Ken Weiner, CTO GumGum
33. Increased adoption of ML platforms
2021 will be the year of the reopening and reset for many businesses that have come to realize that they need to be more agile and data-driven in a more uncertain world. The old rules only partially apply. So more automation and Machine Learning will be used to chart the course ahead in a cost-effective manner.
We expect increased adoption of ML platforms and a higher willingness to invest in change management to reengineer processes to allow for more decision engineering even in more traditional industries.
Manufacturing has been a global locomotive during the pandemic. It is giving way to more capital spending to upgrade existing systems and consequently, it will be one of the biggest beneficiaries of the impending automation wave.
Healthcare has proven front and centre, with a very visible role during the COVID debacle. So it will also consume more ML-driven solutions, albeit in a more patchy fashion due to regional and local wrinkles in the regulatory landscape.
Atakan Cetinsoy, Vice President BigML
34. HyperAutomation becomes a buzzword
Hyperautomation is set to become the new buzzword in the AI and automation space.
Hyperautomation grows from and expands on a variety of automation and process management tools and techniques. For example, robotic process automation, advanced business analytics, machine learning, deep learning, etcetera.
In other words, hyper-automation is the convergence of available automation technologies with other software and artificial intelligence functionality. It represents a culmination of all the advances made in the AI and automation industry so far.
And with this exciting culmination, we can expect to see more businesses looking to evolve and “hyperautomate” processes in 2021 and beyond.
Roxanne Abercrombie, Content Manager ThinkAutomation
35. More state scrutiny of AI
The tremendous power of AI is not well understood at the moment by governments and the general public. I expect that to change soon, maybe even this year.
It only takes a major incident, such as a deepfake video with political or diplomatic consequences, to bring the harmful applications of AI into the spotlight. After such an incident, I expect more scrutiny of AI from state authorities, as well as a fertile ground for conspiracy theories.
At the same time, this will increase interest from many parties; we are already seeing an increase in demand for bare metal computing resources for AI for both legitimate and illegitimate use.
Dragos Baldescu, System Administrator Bigstep
36. AI will improve the Education system
I highly believe in the positive contribution of AI and education is one area that will greatly benefit from AI.
With the current situation pushing online education as the default medium of education, AI will further improve the education system to make it more engaging and teach meaningful skills-based upon individual capability.
Since every student is different and so are their learning capabilities, AI can be a teacher which everyone wants. It can teach you with the pace which is natural to you, which means an improved learning experience rather than a stressful one where you will keep pushing yourself even if you are not understanding anything.
AI can deliver the content in a much progressive way which is not possible by traditional methods. It can also test the application part which traditional method doesn’t as a skill is useless if you can’t apply it and AI can provide scenarios and gamify whole things to give you opportunities to apply the knowledge you are learning.
In short, you will have a companion, teacher, friends which understand you better than anyone else.
Javin Paul, Founder java67.com
37. The deep learning system is converging
We have seen the prosperity and diversity of deep learning systems since 2012. That’s a 10 years golden age of building new hardware and software.
Despite that, many established businesses and startups are putting efforts into it still, the convergence of the DL systems is ongoing. Some systems have stopped maintenance or faded out, namely Caffe and MXNet; many others will die before be they become well-known.
The torrent of the times is unstoppable. After many years, we may remember the 2010s of DL systems just like the 1990s of microarchitecture.
Zhenhua WANG, Founder Jackwish.net
38. Use of AI in drug discovery
Artificial intelligence (AI) and machine learning have steadily made their way into life sciences, often being utilised in areas such as diagnostics and molecule screening.
Within pharma it’s likely we’ll see these technologies being used in screening processes to speed up drug discovery and to examine how different molecules can treat different tumour types.
The vast amounts of data needed for drug development make AI and machine learning useful tools for researchers and could help result in reduced development times and ultimately bring medicines to patients sooner.
Reece Armstrong , Editor European Pharmaceutical Manufacturer
39. AI/ML adoption across many industries
AI/ML will continue to be adopted across many industries and organizations as a way to augment the workforce. Your example, AI for moderation, provided a good view into the possibilities. That is/was an incredibly hard problem for any human labour force to solve on its own.
Similar use cases are being deployed in healthcare, finance, education, logistics, government policy and more.
As the demand for services increase and the market size of many service providers expand to almost global size, more tools will be needed to help aid organizations deliver on their promises and create value. Expect to see more adoption, at a faster pace.
We are creating an immense amount of data every day. We really haven’t tapped into the possibilities. I also believe AI/ML aren’t singular technologies but part of a broader stack of technologies that also deal with decentralization and inclusive value creation.
The future looks promising.
Niheel Patel, Bytes.com
40. Continued growth in AI adoption
Due to the COVID-19 pandemic, there has been unprecedented business changes globally, so it will be normal to see continued growth in AI adoption to help businesses digitalise and build for the future.
Consequently, Explainable AI would potentially move to the forefront as businesses seek to understand the impact of AI solutions before adoption.
Startups that are able to integrate that as a value proposition should see better growth opportunities in 2021.
Jessen Siew, Digital Manager SGInnovate
41. AI adoption will increase as it starts to become a commodity
There’s a lot of research going on in the world of AI today, there’ no doubt about that.
But we’re at a stage where AI has become very affordable to a lot of smaller companies not just in terms of cost, but also in terms of technology. You don’t need researchers in your payroll to build AI into your product anymore. You just need developers who know how to integrate third-party services into your codebase.
Companies such as Amazon (with AWS) and Google (with GCP) are already providing a lot of basic AI capabilities as APIs for a relatively low cost.
You can utilize these “AI as a service” products to easily add some intelligence to your product. With advancements like these in the services market, anybody and everybody can easily adopt AI as a commodity.
Sunny Srinidhi, Data Scientist contactsunny.com
42. One of the FAANGM gets exposed for their data sourcing practices
AI is already a mainstream topic as it triggers questions around equality, diversity, racism and the impact on society.
The focus and scrutiny will only increase in the future. The media will look for new areas to highlight and expose companies to negative PR.
How? AI is based upon data that is often unstructured and requires human intervention to make it machine-readable for ML. This process is labor intensive and very manual in nature, often requiring millions of manual tasks by humans.
There’s a wide range of data sourcing options available in the market, ranging from ethical and economically enabling to potential human exploitation.
In 2021 the business practices for how their data is created will come into question. Plausible deniability won’t be accepted, as the ethics of their data sourcing will come into question.
43. AI Adopters Will Look to Multi-Cloud Architectures
Today, every major CSP is offering a number of AI/ML services, releasing more at a steady clip. However, there is variability in capabilities and strengths.
In 2021, organizations will begin to evaluate multi-cloud architectures more in order to accelerate adoption.
And this is for the very reason that building AI/ML pipelines out of multiple CSP services – with the same data – is a faster path to get more value without more in-house expertise.
Matt Wallace, CTO Faction Inc
44. AI will replace us because it is us
Focus on medical issues will be where AI shines in 2021.
Demonstrated with AlphaFold from DeepMind, taking tasks that are very intensive and finding a process to accelerate the timeframes lends itself well to the health sector. I suspect following COVID that more spend/interest in health-tech, in general, will help here.
There will be an increase in expectation of PC’s coming with a decent GPU.
This will be more to support professional use cases. Companies like Intel adding better GPU acceleration will help here. Longer-term I believe we’ll see dedicated silicon for accelerating common AI calculations (something we’re seeing in mobile with neural processing silicon).
While we fear an AI apocalypse, AI (to me) is the next stage of human evolution.
When we learned to use tools, we scaled human capacity. When we learned to think abstractly, we could invent much much faster than other animals. AI is the continuation of that spectrum. It will replace us because it will be us. I doubt any Neanderthals were excited to see the rise of modern man, but unfortunately, we do all die, and improvement will continue.
John-Daniel Trask, Co-founder & CEO Raygun.com
45. COVID-19 will push the adoption of AI
The pandemic might change some priorities.
It will push the adoption of AI for companies that already had it on their roadmap and/or absolutely need a competitive advantage, or postpone the projects of companies that focus on gaining back their core business.
The AI startup industry will be really dynamic, with even more applications in the medical field for example.
Data science will be focusing more and more on deployment of the solutions, and data scientists will be asked to become more hybrid over time and also handle data engineering to a certain extent.
Finally, research-wise, the sustainability of AI will become a central topic and will be considered as one of the important variables when selecting a model.
Maël Fabien, Co-Founder & CEO SoundMap
46. Edge-based AI will increasingly replace cloud-based AI
Despite all the hype, AI failed to help with the pandemic.
We’ll see more applications of deep learning to accelerating drug and vaccine development (following the examples of Baidu’s LinearFold and Google’s AlphaFold), and more investment in robots for hospitals.
On the other hand, natural language processing showed real progress (eg GPT-3) and there will be a market for pre-trained language models. We may even see high-quality videos generated from texts. In order to deliver real-world applications, edge-based AI will increasingly replace cloud-based AI.
AI chips will provide on-device processing in consumer gadgets.
AI will significantly improve cybersecurity. But self-driving cars will remain a niche product.
Piero Scaruffi, Founder Scaruffi.com
47. We expect to see accelerated adoption of AI in most sectors.
In the course of this year, firms that have larger existing investments will grow faster in terms of revenue, employment, and operational efficiencies.
Due to the widespread economic effects of COVID, certain highly-impacted sectors (e.g. retail, hospitality, entertainment) may increase their reliance on AI and automation technologies as they recover.
As a result, business models may shift from the pre-pandemic status quo. Lastly, there’ll be a risk of concentrated technology adoption leading to anti-competitive and monopolistic behaviours in markets such as retail, information, and the financial sector, with negative effects on choice and value for consumers.
James Hodson, CEO AI for Good Foundation
48. More AI in environmental, social and governance (ESG)
Financial institutions have been working with artificial intelligence and machine learning for years. However, in 2021, they will ramp up efforts with respect to ESG.
Responsible and sustainable investing requires listed companies, exchanges, rating agencies, fund managers, institutional investors and regulators to properly integrate huge amounts of data into risk and investment processes, along with reporting and other functions.
There’s absolutely no way this can be done without applying AI.
Jame DiBiasio, Founder Digital Finace Group
49. AI will become accessible to SMEs
Today AI is a real hype, many people talking about it. But just as any new technology AI is expensive to implement.
This will change in the near future, for example through easy to use platforms aimed at business users rather than data scientists. This will lead to a wave of democratization of AI and will make the technology also accessible for SME’s and useful for smaller business cases.
Pauwel Grepdon, Co-founder Trendskout
50. AI will transform more into Augmented Intelligence
I think Artfical Intelligence will start being seen more as augmented intelligence.
Instead of it being seen as a stand-alone application, AI will transform more into Augmented Intelligence, helping people in increasing efficiency in existing workflows.
For instance, take robotic process automation, or AI helping in filling a form faster by predicting the next values, based on what has already been filled. Or AI helping with writers block by generating ideas on what to write, or AI helping in database compression by identifying what keys are best to compress based on analyzing usage data, or AI helping in writing SQL queries.
I see the future of AI as being more of an assistant rather than stand alone applications like self driving cars or robotics.
Abhishek K, Chief AI Architect SublimeAI Inc
51. Adoption as a credible assistant in a clinical environment
We hope that in 2021 Artificial Intelligence will be adopted more as a credible assistant for doctors in a clinical environment rather than mainly used in a research setting.
If adopted in a clinical environment, we feel that AI can directly assist in aiding treatment and therefore benefit the quality of life for patients more directly.
We believe that this can be achieved if AI is perceived as an assistant to the doctor rather than replacing the doctor.
Marius Wellenstein, CEO WSK Medical
52. Increased AI adoption for cost-effective business solutions
In technical terms, when we’re talking about machine learning in artificial intelligence we refer to reduced manual analysis and larger dataset predictions.
Instead of staffing RiskOps agents together, who will set up static rules for every use case, the use of AI will be able to process enormous amounts of data. No human could trawl through as much payment info, login details, and IP addresses as a computer or machine processing them.
So most online businesses and software companies with large datasets will continue the process of adopting AI in order to integrate more cost-effective solutions into their business in 2021.
Robert Kormoczi, Content Distribution Manager SEON
53. More hardware accelerators based on AI & ML algorithms
As the complexity of AI algorithms increase, typical CPUs can no longer provide the required processing power under the energy constraints.
Specialized hardware accelerators, tailored-made on AI and ML algorithms will be adopted on more devices on the cloud and on the edge.
Programmable accelerators that offer high performance and flexibility, like FPGAs, will increase their market share and popularity with AI implementations. The required software stack for easy FPGA deployment and integration with data science platforms will play a critical role in the widespread adoption of FPGA in the AI world.
Chris Kachris, CEO & Co-Founder InAccel.com
54. Forced government regulations on AI
In 2021 we should see more transformer based models in every field, vision transformers are going to improve but won’t replace CNNs because of their expensive nature.
Medical AI is going to increase in value, becoming one of the “hot” topics.
Countries might force some regulations on AI explainability, especially in critical systems. That might force some of the researchers to focus on XAI.
Two major players in the AI world are still the US and China. With the help of hardware manufactures we should be able to see some other types of networks on the rise (like graph neural networks).
Kemal Erdem, ML Engineer QuarkOwl LTD
55. AI/ML becomes more accessible
In 2021, ML/AI will become more accessible to a broader base of users.
To date, it’s been mainly up to data scientists to use the automation technologies to drive the business and gather data, but that is changing to include anyone in the organization who needs data access to make more intelligent decisions.
Justin Borgman, CEO Starburst
56. I predict big changes for AI in healthcare for 2021 and the immediate subsequent years.
The pandemic has taught us that previous barriers and red tape can be quickly overcome when we have a significant motivator, and COVID-19 provided just that.
In a matter of months, a number of AI solutions were deployed in healthcare systems including an EIT Health project which uses AI to analyse patient data and predict, stratify and personalise the treatment of COVID-19 patients.
This solution reduced mortality by 50% and has been scaled to hospitals across Spain and the Netherlands already.
Now that we have the confidence with quickly implementing, and seeing the vast value that can be derived, I believe healthcare will look to leave behind some of its perceived doubts and implement AI across various levels of healthcare…
…from prediction and prevention of disease, diagnosis and prognosis, capacity and resource allocation and of course patient-facing solutions to improve connectivity and quality of care.
Jan-Philipp Beck, CEO EIT Health
57. ML will be used to predict the evolution of the current Coronavirus
We’ve known for a while that Machine Learning can be used to predict when and where a new virus outbreak might occur, but my prediction for this year is that ML will be used to predict the evolution of the current Coronavirus.
ML algorithms can be created to make random mutations to the in-silico DNA sequence of the virus. From knowledge of fundamental biology, these algorithms should be able to predict how the new protein folds, how it behaves and, more importantly, what the surface of the protein looks like.
From this knowledge, potential vaccines can be designed to target the surface proteins in much the same way as the current vaccines do.
Supercomputers could run through millions of these mutations and a bank of potential future Coronavirus strains could be highlighted for more study to identify the most likely candidates – and therefore the vaccine modifications that we’re likely to need next.
Lee Baker, CEO Chi-Squared Innovations
58. AI will play an ever-increasing role in accessibility and digital inclusion.
Artificial intelligence helps blind people (such as myself) interpret unlabelled images online and easily snap printed text to be read by our phones.
The machine learning built-in to biometrics means that a thumb can provide security to online shopping or banking where alternatives are often confusing, multi-step and time-critical processes.
Yes – today every one of us is using AI in our daily lives regardless of whether or not we have a disability or impairment of any kind.
Each new advancement in AI throughout 2021 and beyond, however, will be the critical key for us as disabled people; the key to unlocking doors in our daily lives that would otherwise remain firmly shut in our (machine-readable) faces.
Robin Christopherson, Head of Digital Inclusion AbilityNet
59. Accelerated use of ML to optimize underlying business drivers
Machine Learning is used primarily as a prediction tool to understand what will happen.
In 2021, I expect more sophisticated companies to focus on using ML to understand the underlying business drivers they can optimize to best affect the future.
In order to gain this understanding, you need to determine the limited set of true drivers and high-order inter-combinatory effects within a large number of data columns, as well as integrate ML with an optimization engine.
An example of this is our work in Trade Promotion Optimization and Supply Chain Optimization. So moving forward, we’ll see an acceleration of focus on how ML can help drive optimal outcomes for the business.
Jason Glazier, Ph.D. CTO Enterra Solutions
60. AI will help alleviate predicted labour shortages
While investments in AI and machine learning will continue to explode and be adopted in 2021 and going forward, I do not predict mass or substantial unemployment.
The aging of the population will lead to serious labour shortages with 25% of the population over 65 and retired.
AI and machine learning will help alleviate looming predicted and expected labour shortages.
Ian Lee, Ph.D. Associate Professor Carleton University
61. Significant growth in the use of AI to improve traditional medical imaging
I think we will see basic AI making significant inroads into the back-office processes of healthcare, scheduling, billing, coding, and patient flow, e.g., call prompts and semi-autonomous.
Clinical applications may draw the headlines, but the only significant growth will be imaging, which is more fully digitized in a standard way. Electronic medical records and the promise of AI-fueled “Big Data” solutions will remain mired in trying to both standardize and create clean data sets.
AI businesses will continue to cater to the C-suite, who make the purchasing decisions; while continuing to ignore the end-users, physicians, nurses, and other healthcare personnel, often risking significant implementation problems if not outright rebellion.
Charles Dinerstein, Medical Director American Council on Science and Health
62. 2021 will yield amazing, data-centric products and services.
Interest in machine learning has declined slightly in the last year. Part of this may be due to COVID-19, but there are also competing technology topics (e.g., cryptocurrency) that are attracting technically minded people.
But underneath recent changes, a strong pattern persists: data is doubling roughly every couple of years. In fact, I would say that the 2020s will be a data-centric decade.
There will still be a need to transform this growing stream of data into valuable products and services. And with relatively new tools and techniques (machine learning, deep learning, GPT-3, etc), we have powerful tools to do that.
Joshua Ebner, Founder Sharp Sight Labs
63. We foresee an increase in AI-assisted drug discovery this year
COVID-19 created a necessity to increase the speed of drug discovery in the Pharmaceutical industry.
AI was at the core of expediting the research, assisting in analyzing the repurposing of drug candidates to acting as a synthetic control arm for vaccine research. Many of the reservations and challenges surrounding AI in biomedical research were mitigated as a result.
At BioSymetrics, we are already experiencing an increase in awareness and exploration of applying machine learning to pharmaceutical research.
Anthony Iacovone, CEO BioSymetrics Inc
64. 2021 will increasingly see AI as a part of our work and life.
Artificial intelligence cuts across almost all aspects of our lives today.
Whether it is through improved machine learning algorithms that serve up more appropriate content in our streams or automated systems that manage our online conversations (chatbots) and feed our pets.
Arts and culture will gain grounds as artists continue to work with machine learning and neural network models to advance our human creativity.
Finally, 2021 will begin to solve AI’s darker side — bias and echo chambers – offering a possible utopian instead of dystopian view of the future for us all.
Brett Ashley Crawford, Ph.D. Associate Professor Carnegie Mellon University
65. The democratization of AI and Machine Learning technologies continues but is far from being over.
Although most companies do not benefit yet from AI, multiple success stories emerge, motivating the mass to start their AI journey.
I expect 2021 to be a cornerstone year for adoption. It is still paramount to democratize what artificial intelligence can do for businesses and build a solid and concrete roadmap to get there.
There is also a potentially unpopular question that arises in the AI community: should we invest more effort into model deployment and MLOps than into modeling?
In my experience, the answer is usually yes. A great model, poorly deployed is useless, while an imperfect model well deployed can generate benefits. This is the reason why MLOps will continue to gain popularity.
Validation of biases, robustness, performance, reliability, maintainability are becoming more and more popular, and will not be optional sooner than later. In some highly regulated industries, this is already the case, but this will become the norm at some point.
Olivier Blais, Head of Data and Transformation Moov AI
66. We may be able to use text analysis techniques to better understand how schools communicate with the families
Certainly there have been a number of gains made in recent years related to machine learning and artificial intelligence, but I am skeptical that they will have a major impact on the way that research is typically carried out.
This is in large part because of this study on the predictability of life outcomes which found that even crowd-sourced machine learning models (similar to something like Kaggle) did not do much better than a simple baseline comparison model.
Where I do think machine learning has real potential, is in capitalizing on different sources of data.
For example, we may be able to use text analysis techniques to better understand how schools communicate with the families they serve by mining letters and announcements they send home with students.
Similarly, it’s possible that images of schools (both inside and out) might predict important things like achievement, absenteeism, etc.
Daniel Anderson, Research Assistant Professor University of Oregon
67. Technological progress will lead us to faster and more accurate ML models
There is no doubt that AI and ML adoption will continue to grow exponentially as it has been doing for several years already. I believe that we’ll see improvements in certain areas related to autonomous driving, natural language processing, various simulations, and so on.
But the saddest part is that most of the progress in this field is done by major companies that do not share their code with AI and ML community. Without assessing the progress of large companies, I still believe that the coming years will be extraordinary.
Technological progress in GPU and CPU processing and global interest in the ML field will lead us to faster and more accurate models in all fields.
Rokas Balsys, Python Lessons
68. AI integrated hardware will be a key area of innovation
In 2021 we will see continued innovation and adoption of AI technologies across industries.
I predict the success of AI in driving biotech, especially biopharmaceuticals, feeding off the recent success and meeting the needs of public health.
Precision farming will also be a strong area of innovation driven by continued concerns for climate and micro-climate disruption.
Finally, AI integrated hardware will be a key area of innovation fueled by both high global demand and concerns on supply chain shortages.
Kevin M. Purcell, Ph.D. Professor of Data Science Harrisburg University
69. 2021 will be a year of evolution, not revolution, for AI in health tech.
COVID-19 accelerated many aspects of digital healthcare: remote patient monitoring (RPM), smartphone apps as digital therapeutics, and wearable devices for disease detection and even diagnosis.
All of these have already been impacted by AI, and this can only grow. We must make healthcare more efficient and more accessible, especially for under-served communities.
AI has demonstrated that it can sift through mountains of raw data to isolate the nuggets of actionable information. This will increase as health tech develops into a reliable assistant for healthcare professionals.
Alfred Poor, Editor Health Tech Insider
70. Combining of AI, IoT, and cybersecurity to create really smart infrastructure tools
All around the world countries are pushing artificial intelligence and machine learning as the key technologies to drive the advances in Smart Highway and Smart Cities.
With roads, junctions, and the transport infrastructure being covered in IoT based sensors and data collection devices that monitor traffic movements, AI is the only way to mine the masses of data, so judgements can be made based on real-world insights.
One of the major themes of 2021 is the combination of three key technologies bringing AI, IoT, and cybersecurity together to create really smart infrastructure tools.
Anthony Davis, Editor Highways.Today
71. Brain-computer interfaces will get more traction in R&D
Digital clones, i.e. AI replicas of working professionals, will become more prominent and there will be an increased focus on explanation and AI (XAI).
I predict a realisation that explanation services must be provided to all parts of the population, including children and individuals with an intellectual disability.
Finally, brain-computer interfaces will get more traction in R&D with commercialization prospects at the horizon. This includes non-medical applications.
Dr Joachim Diederich, Director Psychology Network Pty Ltd
72. AI will have the biggest impact in industries like construction, oil and gas
AI will continue to transform the way that all industries work, but the impact will be the biggest in industries that have been historically slow to adopt technology like construction, oil and gas, etc.
In part, this is because AI solutions around operations and logistics are growing incredibly fast. So, there are more applications for those industries that are really transformational.
And, in part, because the solutions for other industries are not having the same impact. So, there is a shift occurring between solution readiness and industry readiness that will be an AI inflexion point.
Ben Lamm, CEO Hypergiant [Office of Machine Intelligence]
73. Increase in applications with voice recognition for phone services
Many companies will use the COVID pandemic to introduce solutions based on artificial intelligence, both in health sector (i.e. face mask video detection, virus spread models, etc.) and in retail sector (i.e. sales predictions, opening/closing store optimization).
Due to the high increase of full/partial remote work, we will see an increase in applications that can take advantage of AI like voice detection and recognition for phone services, or text detection and understanding for digital sent of documents.
Chatbots may also increase its use for customer screening and guidance on webpages.
Santiago Morante, Ph.D. Data Science Manager at Telefónica Tech
74. 2021 will bring less disbelief and more success with AI across the landscape.
AI and ML have accelerated during the Pandemic as has all digitization of our work.
It’s become a part of our everyday lives already (think facial recognition and social media AI). Yet in some areas, the top objection is a lack of understanding and trust in AI.
In our industry (software QA), even with millions of tests run and case studies galore on the value of autonomous testing, the average QA person isn’t yet ready to give up manual testing or writing scripts. Change is hard but for those who embrace it, it’s a game-changer.
Kevin Surace, CTO Appvance.com
75. AI / ML models will weave Adventure and Purpose together with Diversity and Outcomes.
In Daniel Pink’s book “Drive,” he highlights autonomy, mastery, and purpose as the key care-abouts for millennials. They have little interest in the traditional 40 hour work week. Instead, their interest aligns with a job that produces an experience around adventure and purpose.
AI/ML models can assist in pairing candidates with companies that align with their key care-abouts. In return, companies will radically improve their potential for outcomes and diversity with these models.
For companies looking to onboard the next generation, finding new teammates that bring a wide and diverse frame of perspectives generate a more complete view of possibilities.
Darryl Worsham, CMO Growth Acceleration Partners
76. PC hardware will be an integral part of AI and ML
We expect to see PC hardware continue to have an integral role in Machine Learning and AI as researchers continue to get access to next gen GPUs.
It’s incredible to think about a world where our limitations are only our own creativity and ability to innovate, but we’re close to achieving that goal.
2021 will be a very interesting year for the industry.
Josh Covington, Managing Director Velocity Micro
77. Growth in ML development standards
AI is making its way into more conventional, non-tech-first business while the tech sector continues to grow full force.
Our prediction would be that the best practices and standards around ML development and productionization will become a must-have for teams in 2021.
Similar to software engineering embracing agile best practices 20 years ago.
Jenifer De Figueiredo, Community Manager Iterative AI
78. Machine learning will become mainstream
Machine learning will become mainstream in industries that have been largely undisrupted by technology, mainly by tech-enabled challengers.
The conversation around AI will move towards a higher level of machine intelligence such as cognitive computing, reasoning, and creation of empathetic digital companions that will be embedded in our lives in years to come.
Dor Skuler, CEO Intuition Robotics
79. There will be robust research in AI approaches to improving software quality.
Machine learning techniques will be used to better detect errors (both syntax and logic) prior to software release. These types of errors often lead to vulnerabilities that are exploited by cybercriminals.
Dr. Kevin Huggins, Professor of Computer Science Harrisburg University
80. Instances of AI gone wrong
Our team believes with the growing prevalence of AI/ML, this year will have several public instances of AI-gone-wrong, whether it be bias, security, or other major incidents brought on by mistakes or bad actors.
Brandon Kessler, CEO Devpost.com
I must admit.
This was quite an interesting read.
Especially, when artificial intelligence gets used in recruiting… to automate shortlisting and conduct interviews.
However, I still think we have a really long way to go when it comes to making machines really think like humans.
An example on point is using AI for recruiting. It would take an immense amount of holy grit and just being smart overall to make a machine conduct a job interview that can be deemed conclusive. Not to mention the huge datasets that you’ll need to train a model effectively.
On the flip side, though, there are a ton of free and open-source artificial intelligence tools that have made this, and much more possible.
Think about a car driving itself on the road. You quickly get the impression that nothing is really impossible with artificial intelligence and machine learning.
Why not get started learning AI today?
Here are some free resources for learning AI that I put together to make this journey more fun and easier.
If you are really enthusiastic about learning machine learning (tongue twister), these online courses and platforms will provide you with all the theory you need on calculus, algebra, technically theory and practical AI/ML application programming.
Finally, you’ll use this guide to get an entry-level data science job.
That being said, I’d love to know your thoughts about these AI predictions for 2021.
What do you think will happen in the course of 2021 with respect to artificial intelligence, machine learning and their applications in various industries?
Please share your thoughts in the comments below.