Using voice is powerful way to interact with an interface because it’s spontaneous, intuitive, and enables one to interact with technology in the most natural way possible, which is why Amazon is investing heavily in its voice services, writes WERNER VOGELS, CTO at Amazon.com.
At Amazon, we are heavily invested in machine learning (ML), and are developing new tools to help developers quickly and easily build, train, and deploy ML models. The power of ML is in its ability to unlock a new set of capabilities that create value for consumers and businesses. A great example of this is the way we are using ML to deal with one of the world’s biggest and most tangled datasets: human speech.
Voice-driven conversation has always been the most natural way for us to communicate. Conversations are personal and they convey context, which helps us to understand each other. Conversations continue over time, and develop history, which in turn builds richer context. The challenge was that technology wasn’t capable of processing real human conversation.
The interfaces to our digital system have been dictated by the capabilities of our computer systems—keyboards, mice, graphical interfaces, remotes, and touch screens. Touch made things easier; it let us tap on screens to get the app that we wanted. But what if touch isn’t possible or practical? Even when it is, the proliferation of apps has created a sort of “app fatigue”. This essentially forces us to hunt for the app that we need, and often results in us not using many of the apps that we already have. None of these approaches are particularly natural. As a result, they fail to deliver a truly seamless and customer-centric experience that integrates our digital systems into our analog lives.
Voice becomes a game changer
Using your voice is powerful because it’s spontaneous, intuitive, and enables you to interact with technology in the most natural way possible. It may well be considered the universal user interface. When you use your voice, you don’t need to adapt and learn a new user interface. Voice interfaces don’t need to be application-centric, so you don’t have to find an app to accomplish the task that you want. All of these benefits make voice a game changer for interacting with all kinds of digital systems.
Until 2-3 years ago we did not have the capabilities to process voice at scale and in real time. The availability of large scale voice training data, the advances made in software with processing engines such as Caffe, MXNet and Tensflow, and the rise of massively parallel compute engines with low-latency memory access, such as the Amazon EC2 P3 instances have made voice processing at scale a reality.
Today, the power of voice is most commonly used in the home or in cars to do things like play music, shop, control smart home features, and get directions. A variety of digital assistants are playing a big role here. When we released Amazon Alexa, our intelligent, cloud-based voice service, we built its voice technology on the AWS Natural Language Processing platform powered by ML algorithms. Alexa is constantly learning, and she has tens of thousands of skills that extend beyond the consumer space. But by using the stickiness of voice, we think there are even more scenarios that can be unlocked at work.
Helping more people and organizations use voice
People interact with many different applications and systems at work. So why aren’t voice interfaces being used to enable these scenarios? One impediment is the ability to manage voice-controlled interactions and devices at scale, and we are working to address this with Alexa for Business. Alexa for Business helps companies voice-enable their spaces, corporate applications, people, and customers.
To use voice in the workplace, you really need three things. The first is a management layer, which is where Alexa for Business plays. Second, you need a set of APIs to integrate with your IT apps and infrastructure, and third is having voice-enabled devices everywhere.
Voice interfaces are a paradigm shift, and we’ve worked to remove the heavy lifting associated with integrating Alexa voice capabilities into more devices. For example, Alexa Voice Service (AVS), a cloud-based service that provides APIs to interface with Alexa, enables products built using AVS to have access to Alexa capabilities and skills.
We’re also making it easy to build skills for the things you want to do. This is where the Alexa Skills Kit and the Alexa Skills Store can help both companies and developers. Some organizations may want to control who has access to the skills that they build. In those cases, Alexa for Business allows people to create a private skill that can only be accessed by employees in your organization. In just a few months, our customers have built hundreds of private skills that help voice-enabled employees do everything from getting internal news briefings to asking what time their help desk closes.
Just like Alexa is making smart homes easier, the same is possible in the workplace. Alexa can control the environment, help you find directions, book a room, report an issue, or find transportation. One of the biggest applications of voice in the enterprise is conference rooms and we’ve built some special skills in this area to allow people to be more productive.
For example, many meetings fail to start on time. It’s usually a struggle to find the dial-in information, punch in the numbers, and enter a passcode every time a meeting starts. With Alexa for Business, the administrator can configure the conference rooms and integrate calendars to the devices. When you walk into a meeting, all you have to say is “Alexa, start my meeting”. Alexa for Business automatically knows what the meeting is from the integrated calendar, mines the dial-in information, dials into the conference provider, and starts the meeting. Furthermore, you can also configure Alexa for Business to automatically lower the projector screen, dim the lights, and more. People who work from home can also take advantage of these capabilities. By using Amazon Echo in their home office and asking Alexa to start the meeting, employees who have Alexa for Business in their workplace are automatically connected to the meeting on their calendar.
Voice interfaces will really hit their stride when we begin to see more voice-enabled applications. Today, Alexa can interact with many corporate applications including Salesforce, Concur, ServiceNow, and more. IT developers who want to take advantage of voice interfaces can enable their custom apps using the Alexa Skills Kit, and make their skills available just for their organization. There are a number of agencies and SIs that can help with this, and there are code repositories with code examples for AWS services.
We’re seeing a lot of interesting use cases with Alexa for Business from a wide range of companies. Take WeWork, a provider of shared workspaces and services. WeWork has adopted Alexa, managed by Alexa for Business, in their everyday workflow. They have built private skills for Alexa that employees can use to reserve conference rooms, file help tickets for their community management team, and get important information on the status of meeting rooms. Alexa for Business makes it easy for WeWork to configure and deploy Alexa-enabled devices, and the Alexa skills that they need to improve their employees’ productivity.
The next generation of corporate systems and applications will be built using conversational interfaces, and we’re beginning to see this happen with customers using Alexa for Business in their workplace. Want to learn more? If you are attending Enterprise Connect in Orlando next week, I encourage you to attend the AWS keynote on March 13 given by Collin Davis. Collin’s team has focused on helping customers use voice to manage everyday tasks. He’ll have more to share about the advances we’re seeing in this space, and what we’re doing to help our customers be successful in a voice-enabled era.
When it comes to enabling voice capabilities at home and in the workplace, we’re here to help you build.
Now IBM’s Watson joins IoT revolution in agriculture
Global expansion of the Watson Decision Platform taps into AI, weather and IoT data to boost production
IBM has announced the global expansion of Watson Decision Platform for Agriculture, with AI technology tailored for new crops and specific regions to help feed a growing population. For the first time, IBM is providing a global agriculture solution that combines predictive technology with data from The Weather Company, an IBM Business, and IoT data to help give farmers around the world greater insights about planning, ploughing, planting, spraying and harvesting.
By 2050, the world will need to feed two billion more people without an increase in arable land . IBM is combining power weather data – including historical, current and forecast data and weather prediction models from The Weather Company – with crop models to help improve yield forecast accuracy, generate value, and increase both farm production and profitability.
Roric Paulman, owner/operator of Paulman Farms in Southwest Nebraska, said: “As a farmer, the wild card is always weather. IBM overlays weather details with my own data and historical information to help me apply, verify, and make decisions. For example, our farm is in a highly restricted water basin, so the ability to better anticipate rain not only saves me money but also helps me save precious natural resources.”
New crop models include corn, wheat, soy, cotton, sorghum, barley, sugar cane and potato, with more coming soon. These models will now be available in the Africa, U.S. Canada, Mexico, and Brazil, as well as new markets across Europe and Australia.
Kristen Lauria, general manager of Watson Media and Weather Solutions at IBM, said: “These days farmers don’t just farm food, they also cultivate data – from drones flying over fields to smart irrigation systems, and IoT sensors affixed to combines, seeders, sprayers and other equipment. Most of the time, this data is left on the vine — never analysed or used to derive insights. Watson Decision Platform for Agriculture aims to change that by offering tools and solutions to help growers make more informed decisions about their crops.”
The average farm generates an estimated 500,000 data points per day, which will grow to 4 million data points by 2036 . Applying AI and analysis to aggregated field, machine and environmental data can help improve shared insights between growers and enterprises across the agriculture ecosystem. With a better view of the fields, growers can see what’s working on certain farms and share best practices with other farmers. The platform assesses data in an electronic field record to identify and communicate crop management patterns and insights. Enterprise businesses such as food companies, grain processors, or produce distributors can then work with farmers to leverage those insights. It helps track crop yield as well as the environmental, weather and plant biologic conditions that go into a good or bad yield, such as irrigation management, pest and disease risk analysis and cohort analysis for comparing similar subsets of fields.
The result isn’t just more productive farmers. Watson Decision Platform for Agriculture could help a livestock company eliminate a certain mold or fungus from feed supply grains or help identify the best crop irrigation practices for farmers to use in drought-stricken areas like California. It could help deliver the perfect French fry for a fast food chain that needs longer – not fatter – potatoes from its network of growers. Or it could help a beer distributor produce a more affordable premium beer by growing higher quality barley that meets the standard required to become malting barley.
Watson Decision Platform for Agriculture is built on IBM PAIRS Geoscope from IBM Research, which quickly processes massive, complex geospatial and time-based datasets collected by satellites, drones, aerial flights, millions of IoT sensors and weather models. It crunches large, complex data and creates insights quickly and easily so farmers and food companies can focus on growing crops for global communities.
IBM and The Weather Company help the agriculture industry find value in weather insights. IBM Research collaborates with start up Hello Tractor to integrate The Weather Company data, remote sensing data (e.g., satellite), and IoT data from tractors. IBM also works with crop nutrition leader Yara to include hyperlocal weather forecasts in its digital platform for real-time recommendations, tailored to specific fields or crops. IBM acquired The Weather Company in 2016 and has since been helping clients better understand and mitigate the cost of weather on their businesses. The global expansion of Watson Decision Platform for Agriculture is the latest innovation in IBM’s efforts to make weather a more predictable business consideration. Also just announced, Weather Signals is a new AI-based tool that merges The Weather Company data with a company’s own operations data to reveal how minor fluctuations in weather affects business.
The combination of rich weather forecast data from The Weather Company and IBM’s AI and Cloud technologies is designed to provide a unique capability, which is being leveraged by agriculture, energy and utility companies, airlines, retailers and many others to make informed business decisions.
 The UN Department of Economic and Social Affairs, “World Population Prospects: The 2017 Revision”
 Business Insider Intelligence, 2016 report: https://www.businessinsider.com/internet-of-things-smart-agriculture-2016-10
What if Amazon used AI to take on factories?
By ANTONY BOURNE, IFS Global Industry Director for Manufacturing
Amazon recently announced record profits of $3.03bn, breaking its own record for the third consecutive time. However, Amazon appears to be at a crossroads as to where it heads next. Beyond pouring additional energy into Amazon Prime, many have wondered whether the company may decide to enter an entirely new sector such as manufacturing to drive future growth, after all, it seems a logical step for the company with its finger in so many pies.
At this point, it is unclear whether Amazon would truly ‘get its hands dirty’ by manufacturing its own products on a grand scale. But what if it did? It’s worth exploring this reality. What if Amazon did decide to move into manufacturing, a sector dominated by traditional firms and one that is yet to see an explosive tech rival enter? After all, many similarly positioned tech giants have stuck to providing data analytics services or consulting to these firms rather than genuinely engaging with and analysing manufacturing techniques directly.
If Amazon did factories
If Amazon decided to take a step into manufacturing, it is likely that they could use the Echo range as a template of what AI can achieve. In recent years,Amazon gained expertise on the way to designing its Echo home speaker range that features Alexa, an artificial intelligence and IoT-based digital assistant.Amazon could replicate a similar form with the deployment of AI and Industrial IoT (IIoT) to create an autonomously-run smart manufacturing plant. Such a plant could feature IIoT sensors to enable the machinery to be run remotely and self-aware; managing external inputs and outputs such as supply deliveries and the shipping of finished goods. Just-in-time logistics would remove the need for warehousing while other machines could be placed in charge of maintenance using AI and remote access. Through this, Amazon could radically reduce the need for human labour and interaction in manufacturing as the use of AI, IIoT and data analytics will leave only the human role for monitoring and strategic evaluation. Amazon has been using autonomous robots in their logistics and distribution centres since 2017. As demonstrated with the Echo range, this technology is available now, with the full capabilities of Blockchain and 5G soon to be realised and allowing an exponentially-increased amount of data to be received, processed and communicated.
Manufacturing with knowledge
Theorising what Amazon’s manufacturing debut would look like provides a stark learning opportunity for traditional manufacturers. After all, wheneverAmazon has entered the fray in other traditional industries such as retail and logistics, the sector has never remained the same again. The key takeaway for manufacturers is that now is the time to start leveraging the sort of technologies and approaches to data management that Amazon is already doing in its current operations. When thinking about how to implement AI and new technologies in existing environments, specific end-business goals and targets must be considered, or else the end result will fail to live up to the most optimistic of expectations. As with any target and goal, the more targeted your objectives, the more competitive and transformative your results. Once specific targets and deliverables have been considered, the resources and methods of implementation must also be considered. As Amazon did with early automation of their distribution and logistics centres, manufacturers need to implement change gradually and be focused on achieving small and incremental results that will generate wider momentum and the appetite to lead more expansive changes.
In implementing newer technologies, manufacturers need to bear in mind two fundamental aspects of implementation: software and hardware solutions. Enterprise Resource Planning (ERP) software, which is increasingly bolstered by AI, will enable manufacturers to leverage the data from connected IoT devices, sensors, and automated systems from the factory floor and the wider business. ERP software will be the key to making strategic decisions and executing routine operational tasks more efficiently. This will allow manufacturers to keep on top of trends and deliver real-time forecasting and spot any potential problems before they impact the wider business.
As for the hardware, stock management drones and sensor-embedded hardware will be the eyes through which manufacturers view the impact emerging technologies bring to their operations. Unlike manual stock audits and counting, drones with AI capabilities can monitor stock intelligently around production so that operations are not disrupted or halted. Manufacturers will be able to see what is working, what is going wrong, and where there is potential for further improvement and change.
Knowledge for manufacturing
For many traditional manufacturers, they may see Amazon as a looming threat, and smart-factory technologies such as AI and Robotic Process Automation (RPA) as a far off utopia. However, 2019 presents a perfect opportunity for manufacturers themselves to really determine how the tech giants and emerging technologies will affect the industry. Technologies such as AI and IoT are available today; and the full benefits of these technologies will only deepen as they are implemented alongside the maturing of other emerging technologies such as 5G and Blockchain in the next 3-5 years. Manufacturers need to analyse the needs which these technologies can address and produce a proper plan on how to gradually implement these technologies to address specific targets and deliverables. AI-based software and hardware solutions will fundamentally revolutionise manufacturing, yet for 2019, manufacturers just have to be willing to make the first steps in modernisation.