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Growing role for AI in fresh produce

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There is a growing role for artificial intelligence within horticulture, experts have claimed – but it is not the silver bullet many people think. Speaking at World of Fresh Ideas, Anthony Atlas, head of product and growth at agronomic machine-learning specialist ClimateAI, outlined the benefits and pitfalls of AI use on farms. Describing AI as "systems that generate predictions from past correlations – a giant pattern-identification machine", Atlas said AI is only as good as the training it receives. He stressed that it is not easy to build, and that there isn't one single system that does everything, but instead each task is done by a separate model trained to perform a particular task. In horticulture, AI is being used as a decision-support system in climate and weather forecasting, imagery interpretation and precision automation of greenhouses. Benefits of AI include more complexity, nuance and power, the ability to cheaply automate repetitive tasks, and the fact it is more lightweight than a supercomputer.


How to mitigate bias in AI

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As the common proverb goes, to err is human. One day, machines may offer workforce solutions that are free from human decision-making mistakes; however, those machines learn through algorithms and systems built by programmers, developers, product managers, and software teams with inherent biases (like all other humans). In other words, to err is also machine. Artificial intelligence has the potential to improve our lives in countless ways. However, since algorithms often are created by a few people and distributed to many, it's incumbent upon the creators to build them in a way that benefits populations and communities equitably.


8 Alternatives to TensorFlow Serving

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TensorFlow Serving is an easy-to-deploy, flexible and high performing serving system for machine learning models built for production environments. It allows easy deployment of algorithms and experiments while allowing developers to keep the same server architecture and APIs. TensorFlow Serving provides seamless integration with TensorFlow models, and can also be easily extended to other models and data. Open-source platform Cortex makes execution of real-time inference at scale seamless. It is designed to deploy trained machine learning models directly as a web service in production.


DataRobot exec talks 'humble' AI, regulation

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Organizations of all sizes have accelerated the rate at which they employ AI models to advance digital business transformation initiatives. But in the absence of any clear-cut regulations, many of these organizations don't know with any certainty whether those AI models will one day run afoul of new AI regulations. Ted Kwartler, vice president of Trusted AI at DataRobot, talked with VentureBeat about why it's critical for AI models to make predictions "humbly" to make sure they don't drift or, one day, potentially run afoul of government regulations. This interview has been edited for brevity and clarity. VentureBeat: Why do we need AI to be humble?


Lack of AI implementation may have cost enterprises $4.26T, Signal AI finds

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AI's potential impact on the U.S. economy could reach into the trillions of dollars, according to a report published this week. Signal AI, which offers a decision augmentation platform infused with AI, interviewed 1,000 C-suite executives in the U.S. for the study. The report found 85% of respondents estimate upwards of $4.26 trillion in revenue is being lost because organizations lack access to AI technologies to make better decisions faster. According to the Signal AI survey, 96% of business leaders said they believe AI decision augmentation will transform decision-making, with 92% agreeing companies should leverage AI to augment their decision-making processes. More than three-quarters of respondents (79%) also noted that their organizations are already using AI technologies to help make decisions.


AI 50 2021: America's Most Promising Artificial Intelligence Companies

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The Covid-19 pandemic was devastating for many industries, but it only accelerated the use of artificial intelligence across the U.S. economy. Amid the crisis, companies scrambled to create new services for remote workers and students, beef up online shopping and dining options, make customer call centers more efficient and speed development of important new drugs. Even as applications of machine learning and perception platforms become commonplace, a thick layer of hype and fuzzy jargon clings to AI-enabled software.That makes it tough to identify the most compelling companies in the space--especially those finding new ways to use AI that create value by making humans more efficient, not redundant. With this in mind, Forbes has partnered with venture firms Sequoia Capital and Meritech Capital to create our third annual AI 50, a list of private, promising North American companies that are using artificial intelligence in ways that are fundamental to their operations. To be considered, businesses must be privately-held and utilizing machine learning (where systems learn from data to improve on tasks), natural language processing (which enables programs to "understand" written or spoken language) or computer vision (which relates to how machines "see"). AI companies incubated at, largely funded through or acquired by large tech, manufacturing or industrial firms aren't eligible for consideration. Our list was compiled through a submission process open to any AI company in the U.S. and Canada. The application asked companies to provide details on their technology, business model, customers and financials like funding, valuation and revenue history (companies had the option to submit information confidentially, to encourage greater transparency). Forbes received several hundred entries, of which nearly 400 qualified for consideration. From there, our data partners applied an algorithm to identify 100 companies with the highest quantitative scores--and that also made diversity a priority. Next, a panel of expert AI judges evaluated the finalists to find the 50 most compelling companies (they were precluded from judging companies in which they have a vested interest). Among trends this year are what Sequoia Capital's Konstantine Buhler calls AI workbench companies--building of platforms tailored to different enterprises, including Dataiku, DataRobot Domino Data and Databricks.


How we deployed an Artificial Intelligence (AI) solution quickly during Circuit Breaker

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At the start of the Circuit Breaker, NParks enforced safe distancing measures at all its parks, gardens and nature reserves, including Park Connectors, Pulau Ubin and parks managed by town councils. This includes crowd estimation and park patrols at these green spaces, which can be laborious and tedious, especially for large public spaces. And this work is very much limited by manpower constraints. To address this problem, NParks rapidly developed a Safe Distance @ Parks website within a short span of 3.5 days for the public to access the park visitorship status. Further to that, the team partnered with GovTech's Data Science and AI Division (DSAID) which set up a squad of 1 AI Engineer, 1 DevOps Engineer and 1 Engagement manager to work out a solution to extract the visitor count from CCTV snapshots and automatically update the Safe Distance @ Parks portal in less than two weeks.


How to Make AI Trustworthy

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Repressive regimes are working hard on artificial intelligence (AI) systems that control populations and suppress dissent. If these efforts succeed, political protests will be a sentimental relic of the past, squashed before they ever get to the streets. Less dramatic but nonetheless serious risks also exist for AI in the enterprise. What if incorrect AI credit scoring stops consumers from securing loans? Or an attack on the AI model of a self-driving vehicle leads to a fatal accident?


Global Big Data Conference

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Every day, researchers are marking new milestones in the technology sphere. Artificial intelligence is reaching unprecedented heights, taking humankind along with it. Artificial intelligence defines the ability of machines or models to think and learn from experience. Starting from smart home applications and delivery systems to giant robots in factories and robotic surgeon, everything in the digital era is powered by artificial intelligence and its sub-technologies. After the technology got congested with many achievements, researchers divided it into different types of artificial intelligence for their ease.


The Future of AI in 2021

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AI is a large part of our world already, affecting online search results and the way we shop. Interest in AI has attracted long-term investments in AI use across several industries, particularly in customer service, medical diagnostics and self-driving vehicles. The increased data available through research has created better algorithms which have enabled more complex AI systems that improve a user's experience with search engines and online translation tools, but also means that businesses can make far more focused sales and marketing drives to customers and financial markets have virtual assistants able to deal with more than the simplest of requests. AI system improvements will involve the processing of massive amounts of data which needs improved computing power and better algorithms and tools. Using cryptography and blockchain has made it easier to build these advances since they can publicly share data whilst keeping company information confidential.