With a number of world crises occupying our each day lives, it’s essential to see the place we will leverage know-how to unravel these onerous human issues. Right this moment, we have now extra entry to information from wearables, medical units, environmental sensors, video seize, and different linked units than we have now had at any level prior to now. When mixed with cloud applied sciences, like pc imaginative and prescient, machine studying, and simulation, we’re beginning to get a glimpse of the place that highly effective mix of data and utility can take us.
The subsequent wave of innovators and inventors, a number of of whom I used to be lucky to have met whereas filming Now Go Build, are already constructing options to reforest the planet, preserve our youth lively, and re-imagine the availability chain from the warehouse to supply. And that is really only the start. As entry to superior know-how turns into much more ubiquitous—as each side of life turns into information that we will analyze—we’ll begin to see a torrent of innovation, and it will proliferate in 2023.
Prediction 1: Cloud applied sciences will redefine sports activities as we all know them
Like music and video, sports activities will change into information streams that we will analyze. The insights that these will unlock within the coming years will remodel the complete sports activities business and redefine what it means to play—and expertise—each recreation.
Sports activities are a part of the human cloth. They transcend time, cultures, and bodily borders. At this second, one of many largest sporting occasions on this planet is going on—the World Cup. An estimated 5 billion individuals are anticipated to observe. Till now, broadcast tv has had the largest affect on the evolution {of professional} sports activities, paving the way in which for what’s as we speak a $500 billion business. The subsequent game-changing know-how developments are on the horizon. Within the coming years, each side of each sport will bear a digital transformation, and it will occur at each degree of play, from youth basketball to skilled cricket.
Top leagues, like the Bundesliga and the NFL, have started using video streams, wearables, IoT sensors, and more, for real-time analytics and insights, but looking ahead, these capabilities will continue to advance, and the technologies will become an omnipresent force in nearly every sport and at every level. Imagine a scenario where a coach can use computer vision and biometric data that is analyzed in the cloud in real time to pull a player before they cramp or concede a goal, replacing them with the most well-rested teammate, something now quantifiable. This simultaneously improves player safety, and increases competitiveness of the game. At this point, the sports themselves will truly start to become a data stream that we can analyze and make decisions on in real time—player hydration, ball movement, field saturation—all of it, aggregated, and richer than anything we see today. And with more data comes further innovation. In the not-so-distant future, we will reach a point where teams are running constant what-if simulations in the background during every game, enabling them to better predict the impact of their decisions in the moment. Technology, itself, will become the competitive foundation for professional sports.
Whether in-person or on a screen, the fan experience will also change. Stadiums will rapidly adopt some of the innovations that we’ve seen in industries like retail, such as Amazon Go stores, where the use of computer vision, sensor fusion, and deep learning will enable ticketless entry and grab-and-go purchasing. We will also start to see the next generation of data overlays and real-time insights that go down to the player level, augmenting the game and bringing sports closer to what we expect in the most visually informative video games today. Co-viewing and personalized viewing experiences will continue to evolve, more closely connecting those 5 billion viewers than ever before.
The sports world is currently on the verge of the biggest revolution it has ever seen, and cloud technologies are at the center of this change.
Prediction 2: Simulated worlds will reinvent the way we experiment
Spatial computing. Simulation. Digital twins. These technologies have been slowly maturing for years, but the everyday impact has been limited. This is quickly changing, and in 2023, the cloud will make these technologies more accessible, in turn enabling a new class of use cases that will be unbound by physical constraints.
Simulations are used to build better race cars, predict weather, and model the stock market. While the problems that simulations can solve are significant, the difficulty of building and running simulations is a barrier for everyday use cases. Companies are constrained by the need for high-powered hardware and a specialized workforce. Take a fluid dynamics simulation for a jet wing or race car as an example, where it may take up to 150 TB of data just to simulate one second of a real-world scenario. However, this is quickly changing with technologies like the recently launched AWS SimSpace Weaver, the first of many simulation technologies that will pave the way for a future where nearly anything in our world can, and eventually will, be simulated. Simulations will help us make better decisions about the roadways we construct, the ways we organize our warehouses, and the ways that we respond to disasters. With simulation, we can peer into the future to see the impacts of our efforts, running numerous what-if scenarios that answer our questions without having to wait and see what the impact might be many years down the line. With a technology like SimSpace Weaver, a company like Terraformation can model the growth of entire forests on their way to achieving the goal of planting 1 trillion trees. As a result, it can ensure a biodiverse, and healthy forest that has the most carbon offset possible.
Another area where I’m seeing a rapid uptick in innovation is spatial computing. Companies are already building specialized hardware and using cloud technologies to capture and create 3D models of nearly any environment. Doing this with just a mobile device will soon be a reality. This democratization will inspire a new wave of innovations in the architecture, construction, commercial real estate, and retail industries. Like video did for the internet, spatial computing will rapidly advance in the coming years to a point where 3D objects and environments are as easy to create and consume as your favorite short-form social media videos are today. Static 2D product images on the internet will become a thing of the past, replaced by 3D models that you can pick up, rotate, and place in your living room as seamlessly as you can see them in a web browser today. But expect more to emerge from these models, such that their intrinsic features can be simulated in your virtual home. A virtual lamp will not only be placed on the floor of your living room — you will be able to turn it on and off, watching how the ambient light interacts with your virtual furniture in real time, and understand the impact it has on your energy consumption. All of this, before ever pushing a “buy now” button.
In 2023, technologies like these will begin to converge. With the increasing integration of digital technologies in our physical world, simulation becomes more important to ensure that spatial computing technologies have the right impact. This will lead to a virtuous cycle of what were once disparate technologies, that begin to be used in parallel by business and consumers alike. The cloud, through its massive scale and accessibility, will drive this next era.
Prediction 3: A surge of innovation in smart energy
Energy-storing surface materials. Decentralized grids. Smart consumption technologies. In 2023, we will see rapid development on a global scale that improves the way we produce, store, and consume energy.
We are in the midst of another energy crisis. Rising costs and reliable access to energy are global problems—they impact everyone. While this isn’t the first time that we have faced an energy crisis, there are several maturing technologies that are beginning to converge, and together, they will enable us to address this like never before.
The environment around us produces more than enough renewable energy. The challenge is actually with storage and on-demand delivery to the systems that need to consume that energy. Amazon is doing work in this space, take for example the 150 MW battery storage system in Arizona that’s offering clear, dependable power to our amenities in that space. However we’re not the one ones. Firms throughout the globe are additionally rapidly innovating on this house. The cloud is enabling supplies analysis science for novel use circumstances, corresponding to integrating power storage into the construction of the objects they goal to energy. Think about a delivery vessel the place the edges of the ship are literally the batteries that energy it on its journey. That is simply the tip of the iceberg—no pun meant. We’re additionally beginning to see breakthroughs in long-duration storage, like molten salt, stacked blocks, and gasoline cells.
One other space is the decentralization of power. With uncertainty round power availability, some communities are turning to microgrids. I like to consider microgrids as neighborhood gardens (however for power), the place neighborhood members use these to maintain themselves, decreasing their reliance on conventional power firms and their growing older infrastructure. In my neighborhood, we have now a small microgrid, the place photo voltaic is collected and shared amongst tenants. As we proceed to see power challenges amplified by geopolitical occasions and local weather fluctuations, microgrids will change into a viable answer for a lot of communities all over the world, and cloud applied sciences will play a job in enabling this. Knowledge from photo voltaic panels, wind farms, geothermal, and hydroelectric energy shall be streamed, saved, monitored, enriched, and analyzed within the cloud. Machine studying shall be used to investigate all power information to foretell utilization spikes and stop outages via redistribution of power at a household-level of granularity.
We may even see IoT-based sensible consumption units actually begin to take off throughout the globe within the coming 12 months. This may result in the subsequent wave of improvements that come up from the brand new observability capabilities that these units present for properties and companies alike. Think about the power financial savings we will get by retrofitting historic buildings with power saving applied sciences.
Within the subsequent few years, we’ll see a fast convergence of all varieties of sensible power applied sciences, as we have now lastly met the edge the place our know-how options can tackle our disaster. Whereas this may occasionally not have the fast affect that all of us want it might, collectively these applied sciences will, essentially and perpetually, change the way in which that we create, retailer, and devour power sooner or later.
Prediction 4: The upcoming provide chain transformation
In 2023, adoption of technologies, such as computer vision and deep learning, will propel the supply chain forward. Driverless fleets, autonomous warehouse management, and simulation are just a few of the optimizations that will lead to a new era in smart logistics and global supply chain.
Something that I’ve reflected on regularly over the past few years is the fragility of the global supply chain. We are reminded of this daily—late deliveries, unavailable products, empty shelves. While Amazon has fine-tuned its supply chains with innovations, like digital freight matching and delivery stations, many companies have continued to struggle with logistical challenges. This is about to change.
This will start with the manufacturing of goods themselves. IoT sensors in factories will proliferate and machine learning will be used to not only predict machine failures, but prevent them. Less downtime means consistent production. Shipping those products across the globe is a whole other challenge. Digital freight networks powered by the cloud will traverse countries, even oceans, providing real-time data that allows carriers to optimize with the most efficient shipping routes and change course in response to inevitable events, such as equipment failures and weather disruptions. Think of it as having real-time insights about the current state and arrival time of goods, but at every level of the supply chain.
These freight networks will set the stage for the first cross-country autonomous truck shipments. The impacts will be felt immediately, with countries like the US currently experiencing a shortage of 80,000 drivers. Through the use of spatial computation, edge computing, and simulation, autonomous trucking is set to have a massive impact on our global supply chain. Why? A human driver can only spend so long behind the wheel before they become distracted, tired, and potentially dangerous. And this is before we consider each country’s specific health and safety regulations. This means that fresh fruits being shipped from southern California can only hope to make it as far as Dallas before they begin to deteriorate. However, an autonomous truck can be on the road for 24 hours. There are no mandated breaks, and the technology never gets tired, impatient, or distracted. Products get where they need to faster, safer, and more efficiently.
Upon arriving at a local warehouse, robotic picking, order-sorting, and automated packing will become more commonplace. We will continue to see this evolve with new innovations in robotics that use AI, computer vision, and precision handling of individual products in a company’s inventory. Autonomous robotics will also begin to play a bigger role in warehousing. Imagine being able to augment a forklift operator, who spends a good portion of time simply searching for products, with a real-time digital twin of the inventory, one that is constantly kept up-to-date using autonomous flying inventory drones.
The key to transforming the supply chain is to use technology to optimize each step along a product’s journey. Starting next year, we will see an acceleration in the development of smart factories, smart equipment, and smart shipping that does just that. Each will play a role in improving worker safety, optimizing inventory management, reducing maintenance costs, and streamlining production processes. The supply chain of the future is digital.
Prediction 5: Custom silicon goes mainstream
Usage of purpose-built chips will rapidly increase in 2023. As a result, the pace of innovation will accelerate as workloads take advantage of hardware optimizations that maximize performance, while lowering energy consumption and reducing cost.
Custom silicon and specialized hardware have been quickly gaining traction in the consumer technology industry. Everything from our laptops, to our cell phones, to our wearable devices are seeing significant leaps in performance with the fabrication and adoption of custom silicon. While adoption has been quick in the consumer space, the same hasn’t been true for business applications and systems, where software and hardware traditionally have longer refresh cycles. However, this will quickly change in the coming years as the accessibility and adoption of custom silicon takes hold.
At AWS, there are an average of 100 million EC2 instances fired up every day (as of this writing). This is in large part due to how closely we’ve worked with customers over the years to understand the type of workloads they are running, and then, determine what we should build next. Like consumer devices, this has led AWS to invest heavily in chip design in recent years. That’s because we know that the workloads companies are running in the cloud can be more performant and more cost-effective running on custom silicon, ones that are purpose-built for specific use cases.
Take machine learning workloads for example. Software engineers have traditionally relied on expensive, power-hungry GPUs to do everything from model building to inference. However, this one-size-fits all approach is not efficient—most GPUs aren’t optimized for these tasks. In the coming years, more engineers will see the benefits of moving workloads to processors specifically designed for things like model training (AWS Trainium) and inference (AWS Inferentia). As this occurs, a brand new wave of innovation will start. By realizing a 50% cost-to-train financial savings with a Trainium-based occasion, or 50% higher performance-per-watt on an Inferentia2-based occasion, engineers and companies alike will take discover, and we’ll start to see a large migration of workloads. The identical shall be true even for generalized functions, the place there are nonetheless advantages to shifting to customized silicon, corresponding to Graviton3-based instances that use as much as 60% much less power for a similar efficiency than comparable EC2 situations.
Price financial savings and efficiency advantages will result in extra experimentation, extra innovation, extra adoption, and ultimately, extra customized silicon for different particular workloads. It’s one other virtuous cycle. Alan Kay as soon as stated, “people who find themselves actually critical about software program ought to make their very own {hardware}.” And within the coming 12 months, people who find themselves actually critical about software program will actually start to benefit from all that customized silicon has to supply.
