The technology has helped to improve congestion by 73 per cent in scenario-testing

Ford and Microsoft are using quantum-inspired computing technology to reduce traffic congestion. Through a joint research pilot, scientists have used the technology to simulate thousands of vehicles and their impact on congestion in the US city of Seattle.

Ford said it is still early in the project but encouraging progress has been made and it is further expanding its partnership with the tech giant.

The companies teamed up in 2018 to develop new quantum approaches running on classical computers already available to help reduce Seattles traffic congestion.

Writing on a blog post on Medium.com, Dr Ken Washington, chief technology officer, Ford Motor Company, explained that during rush hour, numerous drivers request the shortest possible routes at the same time, but current navigation services handle these requests "in a vacuum": They do not take into consideration the number of similar incoming requests, including areas where other drivers are all planning to share the same route segments, when delivering results.

What is required is a more balanced routing system that could manage all the various route requests from drivers and provide optimised route suggestions, reducing the number of vehicles on a particular road.

These and more are all variables well need to test for to ensure balanced routing can truly deliver tangible improvements for cities.

Traditional computers dont have the computational power to do this but, as Washington explained, in a quantum computer, information is processed by a quantum bit (or a qubit) and can simultaneously exist "in two different states" before it gets measured.

This ultimately enables a quantum computer to process information with a faster speed, he wrote. Attempts to simulate some specific features of a quantum computer on non-quantum hardware have led to quantum-inspired technology powerful algorithms that mimic certain quantum behaviours and run on specialised conventional hardware. That enables organisations to start realising some benefits before fully scaled quantum hardware becomes available."

Working with Microsoft, Ford tested several different possibilities, including a scenario involving as many as 5,000 vehicles each with 10 different route choices available to them simultaneously requesting routes across Metro Seattle. It reports that in 20 seconds, balanced routing suggestions were delivered to the vehicles that resulted in a 73 per cent improvement in total congestion when compared to selfish routing.

The average commute time, meanwhile, was also cut by eight per cent representing an annual reduction of more than 55,000 hours across this simulated fleet.

Based on these results, Ford is expanding its partnership with Microsoft to further improve the algorithm and understand its effectiveness in more real-world scenarios.

For example, will this method still deliver similar results when some streets are known to be closed, if route options arent equal for all drivers, or if some drivers decide to not follow suggested routes? wrote Washington. These and more are all variables well need to test for to ensure balanced routing can truly deliver tangible improvements for cities.

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Could quantum computing be the key to cracking congestion? - SmartCitiesWorld

Quantum is having a moment. In October, Google claimed to have achieved a quantum supremacy milestone. In November, Microsoft announced Azure Quantum, a cloud service that lets you tap into quantum hardware providers Honeywell, IonQ, or QCI. Last week, AWS announced Amazon Braket, a cloud service that lets you tap into quantum hardware providers D-Wave, IonQ, and Rigetti. At the Q2B 2019 quantum computing conference this week, I got a pulse for how the nascent industry is feeling.

Binary digits (bits) are the basic units of information in classical computing, while quantum bits (qubits) make up quantum computing. Bits are always in a state of 0 or 1, while qubits can be in a state of 0, 1, or a superposition of the two. Quantum computing leverages qubits to perform computations that would be much more difficult for a classical computer. Potential applications are so vast and wide (from basic optimization problems to machine learning to all sorts of modeling) that interested industries span finance, chemistry, aerospace, cryptography, and more. But its still so early that the industry is nowhere close to reaching consensus on what the transistor for qubits should look like.

Currently, your cloud quantum computing options are limited to single hardware providers, such as those from D-Wave and IBM. Amazon and Microsoft want to change that.

Enterprises and researchers interested in testing and experimenting with quantum are excited because they will be able to use different quantum processors via the same service, at least in theory. Theyre uneasy, however, because the quantum processors are so fundamentally different that its not clear how easy it will be to switch between them. D-Wave uses quantum annealing, Honeywell and IonQ use ion trap devices, and Rigetti and QCI use superconducting chips. Even the technologies that are the same have completely different architectures.

Entrepreneurs and enthusiasts are hopeful that Amazon and Microsoft will make it easier to interface with the various quantum hardware technologies. Theyre uneasy, however, because Amazon and Microsoft have not shared pricing and technical details. Plus, some of the quantum providers offer their own cloud services, so it will be difficult to suss out when it makes more sense to work with them directly.

The hardware providers themselves are excited because they get exposure to massive customer bases. Amazon and Microsoft are the worlds biggest and second biggest cloud providers, respectively. Theyre uneasy, however, because the tech giants are really just middlemen, which of course poses its own problems of costs and reliance.

At least right now, it looks like this will be the new normal. Even hardware providers that havent announced they are partnering with Amazon and/or Microsoft, like Xanadu, are in talks to do just that.

Overall at the event, excitement trumped uneasiness. If youre participating in a domain as nascent as quantum, you must be optimistic. The news this quarter all happened very quickly, but there is still a long road ahead. After all, these cloud services have only been announced. They still have to become available, gain exposure, pick up traction, become practical, prove useful, and so on.

The devil is in the details. How much are these cloud services for quantum going to cost? Amazon and Microsoft havent said. When exactly will they be available in preview or in beta? Amazon and Microsoft havent said. How will switching between different quantum processors work in practice? Amazon and Microsoft havent said.

One thing is clear. Everyone at the event was talking about the impact of the two biggest cloud providers offering quantum hardware from different companies. The clear winners? Amazon and Microsoft.

ProBeat is a column in which Emil rants about whatever crosses him that week.

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ProBeat: AWS and Azure are generating uneasy excitement in quantum computing - VentureBeat

What if we could do chemistry inside a computer instead of in a test tube or beaker in the laboratory? What if running a new experiment was as simple as running an app and having it completed in a few seconds?

For this to really work, we would want it to happen with complete fidelity. The atoms and molecules as modeled in the computer should behave exactly like they do in the test tube. The chemical reactions that happen in the physical world would have precise computational analogs. We would need a completely accurate simulation.

If we could do this at scale, we might be able to compute the molecules we want and need.

These might be for new materials for shampoos or even alloys for cars and airplanes. Perhaps we could more efficiently discover medicines that are customized to your exact physiology. Maybe we could get a better insight into how proteins fold, thereby understanding their function, and possibly creating custom enzymes to positively change our body chemistry.

Is this plausible? We have massive supercomputers that can run all kinds of simulations. Can we model molecules in the above ways today?

This article is an excerpt from the book Dancing with Qubits written by Robert Sutor. Robert helps you understand how quantum computing works and delves into the math behind it with this quantum computing textbook.

Lets start with C8H10N4O2 1,3,7-Trimethylxanthine.

This is a very fancy name for a molecule that millions of people around the world enjoy every day: caffeine. An 8-ounce cup of coffee contains approximately 95 mg of caffeine, and this translates to roughly 2.95 10^20 molecules. Written out, this is

295, 000, 000, 000, 000, 000, 000 molecules.

A 12 ounce can of a popular cola drink has 32 mg of caffeine, the diet version has 42 mg, and energy drinks often have about 77 mg.

These numbers are large because we are counting physical objects in our universe, which we know is very big. Scientists estimate, for example, that there are between 10^49 and 10^50 atoms in our planet alone.

To put these values in context, one thousand = 10^3, one million = 10^6, one billion = 10^9, and so on. A gigabyte of storage is one billion bytes, and a terabyte is 10^12 bytes.

Getting back to the question I posed at the beginning of this section, can we model caffeine exactly on a computer? We dont have to model the huge number of caffeine molecules in a cup of coffee, but can we fully represent a single molecule at a single instant?

Caffeine is a small molecule and contains protons, neutrons, and electrons. In particular, if we just look at the energy configuration that determines the structure of the molecule and the bonds that hold it all together, the amount of information to describe this is staggering. In particular, the number of bits, the 0s and 1s, needed is approximately 10^48:

10, 000, 000, 000, 000, 000, 000, 000, 000, 000, 000, 000, 000, 000, 000, 000, 000.

And this is just one molecule! Yet somehow nature manages to deal quite effectively with all this information. It handles the single caffeine molecule, to all those in your coffee, tea, or soft drink, to every other molecule that makes up you and the world around you.

How does it do this? We dont know! Of course, there are theories and these live at the intersection of physics and philosophy. However, we do not need to understand it fully to try to harness its capabilities.

We have no hope of providing enough traditional storage to hold this much information. Our dream of exact representation appears to be dashed. This is what Richard Feynman meant in his quote: Nature isnt classical.

However, 160 qubits (quantum bits) could hold 2^160 1.46 10^48 bits while the qubits were involved in a computation. To be clear, Im not saying how we would get all the data into those qubits and Im also not saying how many more we would need to do something interesting with the information. It does give us hope, however.

In the classical case, we will never fully represent the caffeine molecule. In the future, with enough very high-quality qubits in a powerful quantum computing system, we may be able to perform chemistry on a computer.

I can write a little app on a classical computer that can simulate a coin flip. This might be for my phone or laptop.

Instead of heads or tails, lets use 1 and 0. The routine, which I call R, starts with one of those values and randomly returns one or the other. That is, 50% of the time it returns 1 and 50% of the time it returns 0. We have no knowledge whatsoever of how R does what it does.

When you see R, think random. This is called a fair flip. It is not weighted to slightly prefer one result over the other. Whether we can produce a truly random result on a classical computer is another question. Lets assume our app is fair.

If I apply R to 1, half the time I expect 1 and another half 0. The same is true if I apply R to 0. Ill call these applications R(1) and R(0), respectively.

If I look at the result of R(1) or R(0), there is no way to tell if I started with 1 or 0. This is just like a secret coin flip where I cant tell whether I began with heads or tails just by looking at how the coin has landed. By secret coin flip, I mean that someone else has flipped it and I can see the result, but I have no knowledge of the mechanics of the flip itself or the starting state of the coin.

If R(1) and R(0) are randomly 1 and 0, what happens when I apply R twice?

I write this as R(R(1)) and R(R(0)). Its the same answer: random result with an equal split. The same thing happens no matter how many times we apply R. The result is random, and we cant reverse things to learn the initial value.

There is a catch, though. You are not allowed to look at the result of what H does if you want to reverse its effect. If you apply H to 0 or 1, peek at the result, and apply H again to that, it is the same as if you had used R. If you observe what is going on in the quantum case at the wrong time, you are right back at strictly classical behavior.

To summarize using the coin language: if you flip a quantum coin and then dont look at it, flipping it again will yield heads or tails with which you started. If you do look, you get classical randomness.

A second area where quantum is different is in how we can work with simultaneous values. Your phone or laptop uses bytes as individual units of memory or storage. Thats where we get phrases like megabyte, which means one million bytes of information.

A byte is further broken down into eight bits, which weve seen before. Each bit can be a 0 or 1. Doing the math, each byte can represent 2^8 = 256 different numbers composed of eight 0s or 1s, but it can only hold one value at a time. Eight qubits can represent all 256 values at the same time

This is through superposition, but also through entanglement, the way we can tightly tie together the behavior of two or more qubits. This is what gives us the (literally) exponential growth in the amount of working memory.

Artificial intelligence and one of its subsets, machine learning, are extremely broad collections of data-driven techniques and models. They are used to help find patterns in information, learn from the information, and automatically perform more intelligently. They also give humans help and insight that might have been difficult to get otherwise.

Here is a way to start thinking about how quantum computing might be applicable to large, complicated, computation-intensive systems of processes such as those found in AI and elsewhere. These three cases are in some sense the small, medium, and large ways quantum computing might complement classical techniques:

As I write this, quantum computers are not big data machines. This means you cannot take millions of records of information and provide them as input to a quantum calculation. Instead, quantum may be able to help where the number of inputs is modest but the computations blow up as you start examining relationships or dependencies in the data.

In the future, however, quantum computers may be able to input, output, and process much more data. Even if it is just theoretical now, it makes sense to ask if there are quantum algorithms that can be useful in AI someday.

To summarize, we explored how quantum computing works and different applications of artificial intelligence in quantum computing.

Get this quantum computing book Dancing with Qubits by Robert Sutor today where he has explored the inner workings of quantum computing. The book entails some sophisticated mathematical exposition and is therefore best suited for those with a healthy interest in mathematics, physics, engineering, and computer science.

Intel introduces cryogenic control chip, Horse Ridge for commercially viable quantum computing

Microsoft announces Azure Quantum, an open cloud ecosystem to learn and build scalable quantum solutions

Amazon re:Invent 2019 Day One: AWS launches Braket, its new quantum service and releases

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Quantum expert Robert Sutor explains the basics of Quantum Computing - Packt Hub

More than half (54%) of cybersecurity professionals have expressed concerns that quantum computing will outpace the development of other security tech, according to a research from Neustar.

Keeping a watchful eye on developments, 74% of organizations admitted to paying close attention to the technologys evolution, with 21% already experimenting with their own quantum computing strategies.

A further 35% of experts claimed to be in the process of developing a quantum strategy, while just 16% said they were not yet thinking about it. This shift in focus comes as the vast majority (73%) of cyber security professionals expect advances in quantum computing to overcome legacy technologies, such as encryption, within the next five years.

Almost all respondents (93%) believe the next-generation computers will overwhelm existing security technology, with just 7% under the impression that true quantum supremacy will never happen.

Despite expressing concerns that other technologies will be overshadowed, 87% of CISOs, CSOs, CTOs and security directors are excited about the potential positive impact of quantum computing. The remaining 13% were more cautious and under the impression that the technology would create more harm than good.

At the moment, we rely on encryption, which is possible to crack in theory, but impossible to crack in practice, precisely because it would take so long to do so, over timescales of trillions or even quadrillions of years, said Rodney Joffe, Chairman of NISC and Security CTO at Neustar.

Without the protective shield of encryption, a quantum computer in the hands of a malicious actor could launch a cyberattack unlike anything weve ever seen.

For both todays major attacks, and also the small-scale, targeted threats that we are seeing more frequently, it is vital that IT professionals begin responding to quantum immediately.

The security community has already launched a research effort into quantum-proof cryptography, but information professionals at every organization holding sensitive data should have quantum on their radar.

Quantum computings ability to solve our great scientific and technological challenges will also be its ability to disrupt everything we know about computer security. Ultimately, IT experts of every stripe will need to work to rebuild the algorithms, strategies, and systems that form our approach to cybersecurity, added Joffe.

The report also highlighted a steep two-year increase on the International Cyber Benchmarks Index. Calculated based on changes in the cybersecurity landscape including the impact of cyberattacks and changing level of threat November 2019 saw the highest score yet at 28.2. In November 2017, the benchmark sat at just 10.1, demonstrating an 18-point increase over the last couple of years.

During September October 2019, security professionals ranked system compromise as the greatest threat to their organizations (22%), with DDoS attacks and ransomware following very closely behind (21%).

Originally posted here:

Will quantum computing overwhelm existing security tech in the near future? - Help Net Security

Attempting to explain quantum computing with the comparison between quantum and classical computing is like comparing the world wide web to a typewriter, theres simply next to no comparison.

Thats not to say the typewriter doesnt have its own essential and commercially unique uses. Its just not the same.

However, explaining the enormous impact quantum computing could have if successfully rolled-out and becomes globally accessible is a bit easier.

Archer Materials Limited (ASX:AXE) CEO Dr Mohammad Choucair outlined the impact quantum computing could have on the finance industry.

In an address to shareholders and academics, Dr Choucair outlined that the global financial assets market is estimated to be worth trillions, and Im sure it comes as no surprise that any capability to optimise ones investment portfolio or capitalise on market volatility would be of great value to banks, governments and everyone in the audience.

Traders currently use algorithms to understand and, to a degree, predict the value movement in these markets. An accessible and operating quantum chip would provide immeasurable improvements to these algorithms, along with the machine learning that underpins them.

Archer is a materials technology-focused company that integrates the materials pulled from the ground with the converging materials-based technologies that have the capability to impact global industries including:

It could have an enormous impact on computing and the electric vehicles industries.

The potential for global consumer and business accessibility to quantum computing is the key differentiator between Archer Materials Ltd. and some of the other players in the market.

The companys 12CQ qubit, invented by Dr Choucair, is potentially capable of storing quantum information at room temperature.

As a result of this, the 12CQ chip could be thrown onto the motherboard of the everyday laptop, or tablet if youre tech-savvy, and operate in coexistence with a classical CPU.

This doesnt mean the everyday user can now go and live out a real-world, real-time simulation of The Matrix.

But it does mean that the laptop you have in your new, European leather tote could potentially perform extremely complex calculations to protect digital financial and communication transactions.

To head the progress of the 12CQ Project, Archer hired Dr Martin Fuechsle, a quantum physicist, who is by no means new to the high-performing Australian quantum tech industry.

In fact, Dr Fuechsle invented the worlds first single-atom transistor and offers over 10 years experience in the design, fabrication and integration of quantum devices.

Archer has moved quickly over the last 12 months and landed some significant 12CQ milestones, including the first-stage assembly of the nanoscale qubit processor chip.

Along with the accurate positioning of the qubit componentry with nanoscale precision.

Both of these being key success factors to the commercial and technological readiness of the room-temperature chip.

Most recently, Archer announced the successful and scalable assembly of qubit array components of the 12CQ room-temperature qubit processor. Commenting on the success, Dr Choucair announced: This excellent achievement advances our chip technology development towards a minimum viable product and strengthens our commercial readiness by providing credibility to the claim of 12 CQ chips being potentially scalable.

To build an array of a few qubits in less than a year means we are well and truly on track in our development roadmap taking us into 2020.

The Archer team has commercial agreements in place with the University of Sydney, to access the facilities they need to build chip prototypes at the Research and Prototype Foundry within the world-class, $150 million purpose-built Sydney Nanoscience Hub facility.

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How quantum computing is set to impact the finance industry - IT Brief New Zealand

Technology to Highlight the Next 10 Years According to a Strategy Expert: Quantum Computing

It is said that quantum computers, quantum computing, will have an impact on human history in the coming years. Bank of Americas strategist said that quantum calculation will mark the 2020s.

Bank of America strategist Haim Israel, the revolutionary feature that will emerge in the 2020s will be quantum calculation, he said. The iPhone was released in 2007, and we felt its real impact in the 2010s. We will not see the first business applications for quantum computing until the end of the next decade.

Strategy expert Haim Israel; He stated that the effect of quantum computing on business will be more radical and revolutionary than the effect of smartphones. Lets take a closer look at quantum computing.

What is Quantum Calculation?

Quantum computation is a fairly new technology based on quantum theory in physics. Quantum theory, in the simplest way, describes the behavior of subatomic particles and states that these particles can exist in more than one place until they are observed. Quantum computers, like todays computers, go beyond the storage of zeros and get enormous computing power.

In October, Google, a subsidiary of Alphabet Inc., claimed that they completed the calculation in 200 seconds on a 53 qubit quantum computing chip using a quantum computer, which takes 10,000 years on the fastest supercomputer. Amazon said earlier this month that it intends to cooperate with experts to develop quantum computing technologies. IBM and Microsoft are also among the companies that develop quantum computing technologies.

Quantum computation; health services can recreate the Internet of objects and cyber security areas:

Israel; quantum computing would have revolutionary implications in areas such as health care, the Internet of things and cyber security. Pharmaceutical companies will be the first commercial users of these devices, he said, adding that only the quantum computers can solve the pharmaceutical industrys big data problem.

Quantum computing will also have a major impact on cyber security. Todays cyber security systems are based on cryptographic algorithms, but with quantum computing these equations can be broken in a very short time. Even the most powerful encryption algorithms in the future will weaken significantly by quantum computation, Ok said Oktas marketing manager, Swaroop Sham.

For investors, Israel said that the first one or two companies that could develop commercially applicable quantum computing in this field could access huge amounts of data. This makes the software of these companies very valuable for customers.

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Technology to Highlight the Next 10 Years: Quantum Computing - Somag News

AWS re:Invent 2019 which concluded last week marked another milestone for Amazon and the cloud computing ecosystem. Some of the new AWS services announced this year will become the foundation for upcoming products and services.

Dart Board

Though there have been many surprises, AWS didnt mention or announce some of the services that were expected by the community. My own predictions for AWS re:Invent 2019 were partially accurate.

Based on the wishlist and what was expected, here is a list of hits and misses from this years mega cloud event:

Hits of AWS re:Invent 2019

1) Quantum Computing Delivered through Amazon Braket

After IBM, Microsoft, and Google, it was Amazons turn to jump the quantum computing bandwagon.

Amazon Braket is a managed service for quantum computing that provides a development environment to explore and design quantum algorithms, test them on simulated quantum computers, and run them on different quantum hardware technologies.

This new service from Amazon lets customers use both quantum and classical tasks on a hybrid infrastructure. It is tightly integrated with existing AWS services such as S3 and CloudWatch.

Amazon Braket has the potential to become one of the key pillars of AWS compute services.

2) Leveraging Project Nitro

Project Nitro is a collection of hardware accelerators that offload hypervisor, storage, and network to custom chips freeing up resources on EC2 to deliver the best performance.

Amazon has started to launch additional EC2 instance types based on custom chips powered by the Nitro System. The Inf1 family of EC2 delivers the best of the breed hardware and software combination to accelerate machine learning model inferencing.

Along with Nitro, Amazon is also investing in ARM-based compute resources. Amazon EC2 now offers general purpose (M6g), compute optimized (C6g), and memory optimized (R6g) Amazon instances powered by AWS Graviton2 processor that use 64-bit Arm Neoverse cores and custom silicon designed by AWS.

Going forward, Amazon will launch additional instance types based on Graviton2 processors that will become cheaper alternatives to Intel x64-based instance types.

3) Augmented AI with Human in the Loop

Remember Amazon Mechanical Turk (MTurk)? A crowdsourced service that delegates jobs to real humans. Based on the learnings from applying automation to retail, Amazon encourages keeping the human in the loop.

More recently, Amazon launched SageMaker Ground Truth - the data labeling service powered by humans. Customers can upload raw datasets and have humans draw bounding boxes around specific objects identified in the images. This increases accuracy while training machine learning models.

With Amazon Augmented AI (Amazon A2I), AWS now introduces human-driven validation of machine learning models. The low-confidence predictions from an augmented AI model are sent to real humans for validation. This increases the precision and accuracy of models while performing predictions from an ML model.

Amazon continues to bring humans into the technology-driven automation loop.

4) AI-driven Code Review and Profiling through Amazon CodeGuru

Amazon CodeGuru is a managed service that helps developers proactively improve code quality and application performance through AI-driven recommendations. The service comes with a reviewer and profiler that can detect and identify issues in code. Amazon CodeGuru can review and profile Java code targeting the Java Virtual Machine.

This service was expected to come from a platform and tools vendor. Given the heritage of developer tools, I was expecting this from Microsoft. But Amazon has taken a lead in infusing AI into code review and analysis.

CodeGuru is one of my favorite announcements from AWS re:Invent 2019.

5) Decentralized Cloud Infrastructure - Local Zones and AWS Wavelength

When the competition is caught up in expanding the footprint of data centers through traditional regions and zones, Amazon has taken an unconventional approach of setting up mini data centers in each metro.

The partnership with Verizon and other telecom providers is a great move from AWS.

Both, Local Zones and AWS Wavelength are game-changers from Amazon. They redefine edge computing by providing a continuum of compute services.

Bonus: AWS DeepComposer

Having launched DeepLens in 2017 and DeepRacer in 2018, I was curious to see how AWS mixes and matches its deep learning research with a hardware-based, educational device.

AWS DeepComposer brings the power of Generative Adversarial Networks (GAN) to music composition.

Misses of AWS re:Invent 2019

1) Open Source Strategy

Open source was conspicuously missing from the keynotes at re:Invent. With a veteran like Adrian Cockroft leading the open source efforts, I was expecting Amazon to make a significant announcement related to OSS.

Amazon has many internal projects which are good candidates for open source. From machine learning to compute infrastructure, AWS has many on-going research efforts. Open sourcing a tiny subset of these projects could immensely benefit the community.

The only open source project that was talked about was Firecracker which was announced last year. Even for that, Amazon didnt mention handing it over to a governing body to drive broader contribution and participation of the community.

The industry expects Amazon to actively participate in open source initiatives.

2) Container Strategy

Containers are the building blocks of modern infrastructure. They are becoming the de facto standard to build modern, cloud native applications.

With Amazon claiming that 80% of all containerized and Kubernetes applications running in the cloud run on AWS, I expect a streamlined developer experience of deploying containerized workloads on AWS.

The current developer experience of dealing with AWS container services such as ECS, Fargate and EKS leaves a lot to be desired.

The only significant announcement from re:Invent 2019 related to containers was the general availability of the serveless container platform based on EKS for Fargate. Based on my personal experience, I found the service to be complex.

Both Microsoft and Google score high on the innovation of containerized platforms and enhancing the developer experience.

AWS has work to do in simplifying the developer workflow when dealing with containerized workloads.

3) VMware Partnership

Surprisingly, there was no discussion on the roadmap, growth and adoption of VMware Cloud on AWS. While the focus shifted to AWS Outposts, there has been no mention of the upcoming AWS managed services on VMware.

Though AWS Outposts are available on vSphere, the GA announcement had little to no mention of Outposts on VMware.

4) Simplified Developer Experience

AWS now has multiple compute services in the form of EC2 (IaaS), Beanstalk (PaaS), Lambda (FaaS) and Container Services offered through ECS, Fargate and EKS (CaaS).

Amazon recommends using a variety of tools to manage the lifecycle of the infrastructure and applications. Customers use CloudFormation, Kubernetes YAML, Cloud Developer Kit (CDK) and Serverless Application Model (SAM) to deal with each of the workloads running in different compute environments.

The current deployment model and programmability aspects of AWS are becoming increasingly complex. There is a need to simplify the developer and admin experience of AWS.

I was expecting a new programmability model from Amazon that would make it easier for developers to target AWS for running their workloads.

5) Custom AutoML Models for Offline Usage

Though AWS launched SageMaker Autopilot and Rekognition Custom Labels in the AutoML domain, it didnt mention about enhancing AutoML-based language services for newer verticals and domains.

Custom models trained through Amazons AutoML services cannot be exported for offline usage in disconnected scenarios such as industrial automation. None of the services are integrated with AWS Greengrass deployments for offline inferencing.

Both Google and Microsoft offer exporting AutoML models optimized for the edge.

Amazon Comprehend service could be easily expanded to support newer verticals and domains such as legal and finance through AutoML.

Though the above announcements and services didnt make it to this years re:Invent, I am sure they are in the roadmap.

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The Hits And Misses Of AWS re:Invent 2019 - Forbes