At last week’s IBM Think conference in Las Vegas, the company took Watson from tool to Law, writes TIANA CLINE.
From Moore’s Law in 1971 to Metcalfe’s Law in 1995 to … Watson’s Law in 2018? IBM is heralding a new digital age – with a bit of chutzpah – with the name of its artificial intelligence engine.
“It’s an exponential moment, when both business and technology architectures change at the same time. It has the potential to change everything,” said IBM chairman, president and CEO Ginni Rometty at the IBM Think conference in Las Vegas last week.
IBM Watson integrates the entire spectrum of data science, artificial intelligence and machine learning to lay a foundation for open and adaptive AI. At IBM Think, the tech giant unveiled a number of new cloud technologies (private, public and on-premises), open AI opportunities for businesses, and the fully-customisable Watson Assistant that takes a new approach in the AI space by only talking to businesses.
IBM’s end goal with Watson is to build a data-driven culture for enterprises. It is asking: how can artificial intelligence (AI) be integrated into every profession or industry and industries to transform workflow? And how can one ensure that the data that is gathered will be secure and accessible, wherever it lives, and that data-driven insights can be turned into competitive advantage?
The contrast is with narrow AI, which is able to perform simple smartphone tasks like distinguishing the difference between a cat and a baby in a camera roll, using machine learning (ML). Watson has been ramped up substantially for broader, more in-depth AI, which encompasses the use of smart data patterns, and blockchain for exponential learning.
“Ultimately, we need to make data incredibly simple and accessible with no assembly required,” said Rob Thomas, general manager for IBM Analytics. “IBM Cloud Private for Data is the only platform in the enterprise with no assembly required. It’s Cloud Agile.”
IBM also unveiled two key partnerships with Apple at IBM Think: IBM Watson Services for Apple’s AI, Core ML, and IBM Cloud Developer Console for Apple.
IBM Watson Services for Core ML will allow companies to create AI-powered apps that securely connect to their enterprise data and can run offline and on cloud. The main differentiator is that the AI continuously learns, adapts and improves through each user interaction.
“All iOS developers can now build applications in devices that run Watson, even if they’re not connected,” said David Kenny, IBM’s senior vice president for IBM Watson and Cloud Platform.
“It’s about getting a better understand of what’s going on.”
The new IBM Cloud Developer Console for Apple provides key tools, like pre-configured starter kits, along with AI, data and mobile services for Apple’s coding language Swift. This enables developers to link to IBM Cloud to build easy-to-code apps that can be integrated with enterprise data and are quick to deploy.
“Watson can help you reimagine your workflows,” said Kenny. “There’s a lot of noise in the AI space, but somebody needed to help the enterprise with deep, vertical expertise. It’s about security, transparency and compliance and we wanted to make it easy for businesses to get started, so we packaged together Watson Assistant.”
Siri or Alexa? Djingo and Cortana? No matter what a company names its voice assistant, there’s a good chance it’s Watson underneath. Enter Watson Assistant: it can be embedded into anything and be used in industry-specific applications where businesses can also white-label the service. This means there is no official Watson Assistant wake-word, such as “Hey Siri”, nor plans for a Watson-branded device to be sold in the shops.
“We’re training Watson Assistant with data which really understands industries,” said Kenny. “We want to make it easier for every developer in the world who is building applications.”
Watson Assistant can be implemented across key industry sectors, from hospitality to banking data, insurance, agriculture and the automotive industry. The overarching idea is to combine AI, cloud and the Internet of Things to help businesses enhance their brand and customer experiences.
IBM Watson Assistant for Automotive, for example, is a digital assistant designed to help the automotive industry understand and interact with drivers and passengers.
In the agriculture space, IBM Watson IoT can analyse farm data like temperature, soil pH and other environmental factors to give farmers insights that can help them make better decisions – and harvest greater yields. On a global scale, Identity Guard is using IBM Watson to fight cyberbullying, using social media and smart AI monitoring tools. A collaboration between IBM Research and the University of Oxford has begun using machine intelligence to simulate and explore more effective malaria policy interventions.
As Watson Assistant develops a deeper understanding of the user, it will be able to include additional factors, such as their location and time of day. The difference between Watson Assistant and voice assistants is that learns through each interaction.
Watson, as an AI platform, can quickly build and deploy chatbots and virtual agents across a variety of channels, including mobile devices, messaging platforms, and even robots.
With Watson, IBM believes that companies won’t need to fight data, but rather use it to accelerate research and discovery, and enrich customer interactions. Adaptive AI isn’t just an advantage, was the underlying message at IBM Think, it’s essential.
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.