Category Archives: Ai

Artificial Intelligence (AI) is a series of systems, developments and techniques that enable machines to calculate actions and data sets. It is a constellation of many different technologies that work together to enable machines to sense, understand, act, and learn at a human intelligence level. AI systems are becoming increasingly complex as they are used in more and more areas of our lives to create forecasts and prediction models.

Popular search engines now make recommendations based on the text users enter. The search engine uses AI to predict what they are trying to find to give them better information. When one uses maps apps on their phone to navigate, AI reads numerous data points and provides the user with updated traffic information in real time. Statistical machine translation methods are used to find patterns in billions of words of translated text, such as United Nations books and records, and then applies these patterns to new translations.

Several companies are using technological advancements in Machine Learning (ML), natural language processing and other forms of AI to make relevant and immediate recommendations for their customers. Modern technologies based on ML and AI are being adopted by the robotics industry to develop robots that can work autonomously and overcome all the challenges they face on the move.

Many see AI as increasing human capacity, but some predict the opposite: that as people become increasingly dependent on machine-controlled networks, their ability to think for themselves will be undermined

By prioritising technology solutions that enable them to harness the power of AI, companies can instantly provide potential buyers with bespoke content and relevant information about them in a virtual world. Human concierges wearing augmented reality (AR) headsets that tell them what customers want before they ask are already a reality.

One company that has succeeded in this approach is a renowned tailor, who uses AI in partnership with human stylists to select clothes for his customers. In fact, it has been found that most people accept AI recommendations when it works in partnership with humans.

While Hollywood films and science-fiction novels often portray AI as humanlike robots conquering the world, the current evolution of AI technology is not scary, but intelligent. Given the scepticism of leaders in modern AI research and the diverse nature of modern narrow AI systems, there is little cause to worry that general artificial intelligence will disrupt society anytime soon.

Many see AI as increasing human capacity, but some predict the opposite: that as people become increasingly dependent on machine-controlled networks, their ability to think for themselves, act independently of automated systems, and interact with one another will be undermined.

As we gather more and more data, ML tools have improved. This ability to process rapid, enormous amounts of data, refine information and find connections is causing AI technology to proliferate. Scientists are using AI to manage data-intensive terrain, refine climate science, make more accurate predictions, and enable society and nature to adapt to the future.

Analysts expect people to become increasingly reliant on connected AI and increasingly complex digital systems. Nevertheless, if we implement these systems wisely, we can continue the process of improving everyday life with positive results.

This article was prepared by collating various publicly available online sources.

Claude Calleja, Executive, eSkills Malta Foundation

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Can we really rely on AI? - Times of Malta

One of the most profound, and perhaps unanticipated impacts of the COVID pandemic is the dramatic changes to the global supply chain, global workforce, and newfound pressures on delivery and logistics. Certainly no one would have imagined that a primarily health-related cause should have such profound economic, workforce, and basic materials impacts.

However, out of challenge comes opportunity. Organizations are re-examining their processes and technologies that deal with all aspects of producing and delivering goods and services to market, from the origination of raw materials to the delivery of finished products. Organizations focused on shipping and logistics are especially realizing the new reality, and certainly UPS, one of the largest delivery and logistics companies in the world is no exception.

Speaking at an upcoming Data for AI event on October 7, 2021, Gregory Brown, Vice President of Strategy and R&D, Advanced Technology Group at UPS and Laura Patel, Principal Data Scientist at UPS explain exactly these impacts and how UPS makes data-driven decisions for AI innovation. In this Forbes interview, Greg and Laura share some insights they will be diving into at the online event.

What are some innovative ways youre leveraging advanced data analytics to benefit UPS?

Gregory Brown, UPS

Greg: We are using advanced data analytics, AI and automation to process more volume, more efficiently with the reliability that our customers have come to expect from us. These tools are critical to increasing the visibility and control over what packages are coming into our network, where those packages are going, and how soon they need to be delivered. Weve also reduced the time in transit between millions of zip code combinations. That translates to more smiles from satisfied customers.

How do you identify which problem area(s) to start with for your data analytics and cognitive technology projects?

Greg: Were always looking for opportunities where our internal and external customer needs intersect. For example, our internal customers in operations may benefit from enhanced utilization of our facilities and vehicles, and our external customers would also benefit with enhanced reliability, visibility, or reduced time in transit. To find the most impactful opportunities, we use a technology-agnostic approach that gives us the flexibility to identify solutions that best fit our business.

What are some of the unique opportunities you have when it comes to data and AI?

Laura Patel, UPS

Laura: Predicting how much, and what type, of packages may enter our network is a unique opportunity to leverage advanced data analytics and AI. We are able to more efficiently determine how 24.7 million packages and documents flow through our network around the world every day. This increases our teams opportunities to more quickly make data-driven decisions throughout our global network.

Can you share some of the challenges when it comes to AI and ML adoption?

Laura: Incorporating new technologies, even new applications of existing technologies, across a global enterprise that operates around the clock every day requires an extraordinary amount of planning and preparation. We take extra care to ensure that the adoption of new technologies is done seamlessly, reliably, and securely, and is understood and usable by our employees and customers.

How do analytics, automation, and AI work together at UPS?

Laura: We start with a solid foundation in analytics, building up to AI, which enables us to build to automation. By using a building-block approach, we can identify problems that need to be addressed and build solutions that are robust, replicable and scalable.

What are you doing to develop a data literate and AI ready workforce?

Greg: We attract Masters and Ph.D.-level talent to our company, and also utilize our promote-from-within culture to train our employees to be adept with these technologies. We also make it more accessible for people who are not specialists in AI and data analytics to understand the concepts and incorporate the technologies into their day-to-day responsibilities. This inclusive, team-based approach enables us to apply data analytics and AI across our enterprise.

What AI technologies are you most looking forward to in the coming years?

Greg: Were excited about how technologies will continue to create opportunities to solve complex problems in innovative ways. Technology is increasingly being built into the tools that our employees use, our vehicles, and our facilities. The next step would be making these technologies more portable by utilizing smart infrastructure. A seamless smart infrastructure would unlock opportunities to serve our customers in ways that we only could have dreamed of a few years ago.

Greg and Laura both share that they will be diving into these details in greater depth at the online Data for AI event coming up on October 7, 2021.

Excerpt from:

AI And Advanced Analytics In Shipping And Logistics: An Interview With Gregory Brown And Laura Patel, UPS - Forbes

A new study warns that the American AI industry is highly concentrated in the San Francisco Bay Area and that this could prove to be a weakness in the long run. The Bay leads all other regions of the country in AI research and investment activity, accounting for about one-quarter of AI conference papers, patents, and companies in the US. Bay Area metro areas see levels of AI activity four times higher than other top cities for AI development.

When you have a high percentage of all AI activity in Bay Area metros, you may be overconcentrating, losing diversity, and getting groupthink in the algorithmic economy. It locks in a winner-take-most dimension to this sector, and thats where we hope that federal policy will begin to invest in new and different AI clusters in new and different places to provide a balance or counter, Mark Muro, policy director at the Brookings Institution and the studys coauthor, told WIRED.

The study, titled The geography of AI, ranks nearly 400 US metro areas based on their capabilities in AI, using metrics like AI job listings, early-stage company creation data from Crunchbase, published research, and federal research and development funding. It found that two-thirds of AI activity is in just 15 metro areas, largely along coastlines: the two superstars of San Francisco and San Jose, plus 13 other early adopter locales like Austin and Seattle. Meanwhile, more than half of the metro areas together account for just 5 percent of AI activity.

The impact of AI on peoples everyday lives is expected to grow as more businesses and governments adopt the technology. While automation can grow productivityPwC predicts it will add $3.7 trillion to North American economies by 2030some economists and ethicists fear AI will also accelerate inequality and give more wealth and power to people who are already wealthy and powerful. Cities with the ability to support early-stage AI development and forge talent pipelines for local businesses will reap the benefits as the AI industry continues to grow. Those that dont could potentially get left behind, although increased AI adoption can have downsides, too, like job loss from automation.

Muro says the US would be wise to invest in other parts of the country before AIs regional overconcentration becomes even more entrenched.

The study identifies nearly 90 cities in the United States that have the potential to bring more AI-related jobs and resources to their communities, including major cities like Atlanta, Chicago, Detroit, and Houston, as well as some college towns like Bloomington, Indiana, and Athens, Georgia.

Cognizant of the fact that its difficult to drive AI development without deep research and investment capabilities, Muro and his coauthor Sifan Liu urge cities to develop highly realistic plans to support local AI. They suggest regions focus on plans to attract and retain AI talent, expand education opportunities at high schools and community colleges, and consider tax breaks for businesses in the AI space.

Muro also advocates policy that focuses on AI use cases valuable to local businesses or industries, awards government contracts to local AI companies, and differentiates their city from others. There is plenty of room for AI growth beyond tech companies, as suggested by cities in the studys early adopter category. In Lincoln, Nebraska, American Express is the biggest AI employer, according to an analysis of Burning Glass data performed as part of the study. In Los Angeles and Santa Cruz, California, the cybersecurity company Crowdstrike leads the way. In Washington, DC, Capital One and Booz Allen Hamilton are major employers.

Regional leaders in places like San Diego and Louisville, Kentucky, have taken steps to assess the AI needs of their respective regions.

A 2019 Brookings report predicted that Kentucky would be one of the US states most heavily impacted by job loss due to automation. In April, Brookings Metro laid out a strategy for Louisville, one of the states largest cities, to adapt. That report suggests a partnership with other Midwest and Southeast US cities on AI and data solutions for health care.

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In the US, the AI Industry Risks Becoming Winner-Take-Most - WIRED

Investments in artificial intelligence and machine learning are finally on the rise in healthcare.

While the industry has been slow to adopt AI in comparison to other sectors like financial services and manufacturing with 70% of health systems yet to establish a formal program a recent survey found that 68% of health system executives plan to invest more in AI in the next five years to help reach their strategic goals. And the investments are expected to be significant; the global AI in healthcare market size is estimated to reach $120.2 billion by 2028.

The opportunities for AI in healthcare are widespread, spanning both operational and clinical use cases including fraud prevention, voice-assisted charting, registration, remote patient monitoring and more. AI holds particular promise for connected medical devices and telehealth an integral part of the Internet of Medical Things (IoMT) as it enables faster triage, intake, detection and decision making.

In fact, new patient apps and connected medical devices leveraging AI are already being launched regularly. For example, Google recently introduced a new AI-powered dermatology app that uses image recognition algorithms to provide expert, personalized help by suggesting possible skin conditions based on patient-uploaded photos. A Philips device leverages insights from AI to diagnose and treat oncology patients. And Amwells new telehealth platform enables providers to receive alerts on their patients health status via an AI-powered, automated real-time early warning score system.

While there is significant potential for AI in healthcare, there are also limitations. The primary challenge that has not yet been widely discussed, however, is how best to secure AI-powered connected medical devices from increasingly frequent and complex cybersecurity risks.

Securing the IoMT in the age of AI is imperative

While AI can and often has been used for good, it can also be used to discover and exploit vulnerabilities. For example, the same type of algorithm being implemented in a medical device to more accurately and quickly diagnose cancer may also be used by a bad actor to attack that device. To illustrate, a 2019 study from Ben-Gurion University demonstrated how AI-savvy hackers could manipulate CT and MRI results of lung cancer patients gaining complete control over the number, size and location of tumors.

Both radiologists and AI algorithms were unable to differentiate between the altered and correct scans. This kind of tampering has the potential to impact patient lives, and can also result in insurance fraud, ransomware attacks and other issues for both patients and providers.

Bad actors often need little more than an emulator which enables one computer system to behave like another and a piece of code from the system being targeted in order to successfully program AI to hack a device.

Cyber threats are clearly a significant and increasing challenge for the connected industries. In 2019 alone, cyberattacks on IoT devices increased dramatically, accounting for more than 2.9 billion events. And its estimated that 50 billion medical devices will be connected to clinical systems within the next 10 years, making the IoMT (Internet of Medical Things) industry an increasingly opportune target for hackers. Despite the repercussions of a cyberattack, data shows that many manufacturers are challenged to practice Security by Design due to shortage of knowledge and know-how. According to a recent survey we did, only 13% of IoMT leaders believe their business is very prepared to mitigate future risks, while 70% believe that they are only somewhat prepared at best.

However, there are steps manufacturers can take to protect their devices from the start.

How to ensure AI-enabled devices are secure

Although AI and machine learning models are expensive and time intensive to create, once they are built, they are very easy to replicate. Restricting and preventing access to a system is thus a critical first step in protecting systems from adversaries.

In order for bad actors to successfully attack a system built on AI, they need access to the systems data, or a digital twin, for their algorithms to process. In most cases, machine learning lifting, or emulation of data is possible because the automated system answers thousands of questions without being flagged as a potential threat; with answers to these questions, the bad actors can easily use AI to replicate the system or program, even if its a complex medical device software or process.

Limiting access is thus crucial, and includes a few steps:

Beyond access control and anomaly detection, its also important to harden connected devices against reverse engineering. Manufacturers can use many different tactics and solutions to make the code in their devices difficult to reverse engineer and thereby help keep them secure.

All of these protections should be built into devices during the original R&D process, as it is much more of an arduous task to add cybersecurity once a product is already on the market.

Additionally, its important for medtech manufacturers to ensure the regulatory readiness of their medical devices, particularly as the regulatory landscape continues to evolve. While 80% of medtech executives believe that regulatory compliance is the biggest business benefit of implementing a strong cybersecurity strategy, only four in 10 respondents rated themselves very aware or knowledgeable about forthcoming EU and U.S. cybersecurity regulations. Leveraging an assessment tool can help manufacturers examine their regulatory preparedness and identify any weak spots so they can address them before the device goes to market.

Machine learning has the power to be used both for good and unfortunately for nefarious purposes. As more connected medical devices are built on AI, cybersecurity risks will increase as well and its more important than ever before for manufacturers to implement advanced security protections in the design phase to ensure the safety of healthcare organizations, providers and patients.

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The future of healthcare is dependent on securing AI-powered medical devices - MedCity News

Machine learning on digital interface and blue network background

Machine Learning Operations (MLOps) is on the rise as a critical technology to help to scale machine learning in the enterprise. According to McKinsey, by 2030, ML could add up to 13 trillion dollars back into the global economy by enabling workers in all sectors to improve their output. Furthermore, MarketWatch indicates that, in 2021, the global MLOps market size will be USD million and it is expected to reach USD million by the end of 2027, with a CAGR during 2021-2027. According to IBM by 2023, 70% of AI workloads will use application containers or be built using a serverless programming model, necessitating a DevOps culture. Whats more, according to Algorithmia, 85% of machine learning models never make it to production. For businesses, creating machine learning applications, managing those models and putting them into action is challenging. Different companies, such as DataRobot, have emerged as top machine learning operations tool enablers for the industry to handle these challenges.

Processing, implementing and deploying machine learning models requires specific tools that can solve challenges in the process. The challenge of getting data from aa data to decisions is made more accessible by applying various operations on-device or in the cloud as needed. To do this at scale, businesses need a platform to add support for new ML frameworks through open interfaces. There are several ways to add or remove models and processes.

The leading machine learning operations tools for enterprise are:

BRAZIL - 2021/05/11: In this photo illustration the DataRobot logo seen displayed on a smartphone ... [+] screen. (Photo Illustration by Rafael Henrique/SOPA Images/LightRocket via Getty Images)

DataRobot specializes in automated machine learning for businesses, which eases the process of model development and upkeep within an app or platform. DataRobots suite of products also gives users access to a pre-trained model store. DataRobot offers several features that help businesses get started with ML data pipelines and operations, including a visual debugger for debugging machine learning code.

DataRobot's competitive advantage is the ease of use for non-technical users. DataRobot's user interface enables ML beginners to input data and build a model without in-depth coding knowledge or background. Some unique solutions include the ability to run models in a web browser, prototyping tools to test data pipelines and algorithms before launching them in production, and the ability of DataRobots AutoML suite to choose between hundreds of machine learning algorithms automatically. The model store can add more than 200 open-source frameworks from TensorFlow, SciKit-Learn, XGBoost, PyTorch, and TensorRT.

Some of DataRobot's top customers are Deloitte, Panasonic, US Bank, Lenovo, among others. An example success story is a cross-functional team at Panasonic that used DataRobot to build predictive maintenance models that identified and repaired equipment problems up to 9 days earlier than their previous method. This reduced the number of machine failures and increased productivity by 5%.

H2O is a complete platform for data science and machine learning that enables companies to implement end-to-end workflows from data preparation to model building with one consistent SDK. The company also offers support in developing, deploying and managing models.

H2O's automation engine enables businesses to create, deploy and manage machine learning applications in a visual environment. These environments offer pre-configured workflows for common machine learning tasks like feature engineering, model training and deployment. This is where the competitive advantage comes: it speeds up results for non-technical users who can run experiments from one interface that includes data preparation with automated feature engineering and model training with XGBoost. H2O's platform supports any data type, scales to large clusters of GPUs and integrates with Spark, Python, R and other languages.

Some companies using H20 include global leaders in retail, banking, telecommunications and insurance. An example success story is a telecom company that wanted to analyze customer experience data to predict potential churners. The telecom company reduced churn by 10% and increased the number of customers contacted per month from 30,000 to 100,000.

Close-up of sign with logo on facade of the regional headquarters of ecommerce company Amazon in the ... [+] Silicon Valley town of Sunnyvale, California, October 28, 2018. (Photo by Smith Collection/Gado/Getty Images)

Amazon SageMaker is a platform for data scientists. It was built to address businesses challenges in getting from raw data to production-ready machine learning models. Amazons cloud software enables enterprises to implement end-to-end workflows and create, train, deploy and manage machine learning applications. This eliminates the need for companies to maintain their internal data, science teams.

Amazon SageMaker's competitive advantage is that it offers pre-configured templates for deep learning, reinforcement learning and multi-cloud training across multiple frameworks, like Apache MXNet, TensorFlow and others. Amazon also provides custom configurations for businesses that need a more specific type of model or tool. With support for feature engineering and automatic hyperparameter tuning, Amazon SageMaker speeds up building a model and reduces time spent debugging.

Amazon SageMaker's biggest customers range from Toyota to Nielsen, ExxonMobil to Epic Games. An example success story is Nielsen, which migrated its National Television Audience Measurement platform to AWS and built a new, cloud-native television rating platform that allowed the company to grow its measurement capabilities from measuring 40,000 households daily to more than 30 million households each day.

MLFlow is a machine learning platform that enables collaborative experimentation and tracking. This speeds up the entire process of building, training and deploying models across data teams. MLFlow has an open-source lightweight library for Python developers who want to track experiments on TensorFlow, SciKit-Learn and PyTorch via one API. The company also offers a server product that allows teams to track experiments on Spark via one API.

MLFlow's main competitive advantage is allowing employees outside of the data science team to collaborate on building, training and deploying models. The platform also speeds up time for deploying models and tracking experiments across tools.

Some companies that use MLFlow include Microsoft, Zillow, Facebook, Booking.com and Genpact. For example, Microsoft supports open-source MLflow in Azure Machine Learning to provide its customers with maximum flexibility. This means developers can use the standard MLflow tracking API to track runs and deploy models directly into the Azure Machine Learning service.

HANOVER, GERMANY - MARCH 02: Visitors check out a slimmed down version of the IBM Watson ... [+] supercomputer recently featured on the Jeopardy television game show at the IBM stand at the CeBIT technology trade fair on March 2, 2011 in Hanover, Germany. CeBIT 2011 will be open to the public from March 1-5. (Photo by Sean Gallup/Getty Images)

IBM Watson Machine Learning allows businesses to deploy self-learning models at scale, allowing AI to be used in applications and is available for free or with a price based on workload.

The main competitive advantage of IBM Watson Machine Learning is that it provides the possibility to train, deploy and manage models according to a companys specific requirements. The platform supports the deployment of models on any infrastructure (cloud or on-premises) for many businesses.

IBM Watson Studio is the ideal platform for companies to build their multicolored ModelOps practice. It provides an integrated development environment that allows developers to use the latest cognitive computing tools from within a single package, also part of IBM Machine Learning. This means businesses can develop, build and train models in one place and deploy them on any framework like TensorFlow, SparkML or H20.

An interesting case study is American Airlines. American Airlines needed a new technological platform and a different method of development that would help it provide digital self-service functionality and customer value more swiftly throughout its business. By providing the airline with a common platform, IBM assists it in moving some of its critical applications to the IBM Cloud and using new methods to develop creative apps quickly while improving customer experiences.

Algorithmia is a single platform that covers all aspects of machine learning operations (MLOps). It allows for collaboration between data experts and engineers on complicated applications. 100,000 people are using the service, including UN staff members and Fortune 500 businesses.

The companys main competitive advantages include the ability to ramp up speed and productivity by streamlining data science operations and reducing costs by bringing data science operations in-house. The platform also allows developers to automate data science tasks with code. It enables the creation of workflows for predictive apps using standard tools like Jupyter Notebooks, RStudio, Apache Spark and TensorFlow via a simple drag-and-drop interface.

Customers of Algorithmia include Tevec, EY and Github. According to EY Partner Carl Case, EY successfully used Algorithmia's MLOps solution: Weve reduced false positives in institutional systems by 40-60%, sometimes more, and the real benefit of working with Algorithmia has been taking deployment timelines down and getting models to production.

MLOps tools are essential for enterprises that want to turn their valuable datasets into actionable insights at the pace of digital transformation. These tools focus on model management and deployment, both to the cloud and device. In addition, there is also support for new frameworks as they are released to enable businesses to handle ongoing machine learning operations. The significance of these tools is only expected to grow as enterprises apply machine learning at scale. Lastly, MLOps should leave businesses feeling empowered to test and run their models, eliminating errors and misfires.

Read this article:

Leading MLOps Tools Are The Next Frontier Of Scaling AI In The Enterprise - Forbes

Sep 10, 2021 - by Laura Snider

The National Center for Atmospheric Research (NCAR) is a collaborator on a new $25 million initiative that will use artificial intelligence to improve traditional Earth system models with the goal of advancing climate research to better inform decision makers with more actionable information.

The Center for Learning the Earth with Artificial Intelligence and Physics (LEAP) is one of six new Science and Technology Centers announced by the National Science Foundation to work on transformative science that will broadly benefit society. LEAP will be led by Columbia University in collaboration with several other universities as well as NCAR and NASAs Goddard Institute for Space Studies.

The goals of LEAP support NCARs Strategic Plan, which emphasizes the importance of actionable Earth system science.

LEAP is a tremendous opportunity for a multidisciplinary team to explore the potential of using machine learning to improve our complex Earth system models, all for the long-term benefit of society, said NCAR scientist David Lawrence, who is the NCAR lead on the project. NCARs models have always been developed in collaboration with the community, and were excited to work with skilled data scientists to develop new and innovative ways to further advance our models.

LEAP will focus its efforts on the NCAR-based Community Earth System Model. CESM is an incredibly sophisticated collection of component models that when connected can simulate atmosphere, ocean, land, sea ice, and ice sheet processes that interact with and influence each other, which is critical to accurately project how the climate will change in the future. The result is a model that produces a comprehensive and high-quality representation of the Earth system.

Despite this, CESM is still limited by its ability to represent certain complex physical processes in the Earth system that are difficult to simulate. Some of these processes, like the formation and evolution of clouds, happen at such a fine scale that the model cannot resolve them. (Global Earth system models are typically run at relatively low spatial resolution because they need to simulate decades or centuries of time and computing resources are limited.) Other processes, including land ecology, are so complicated that scientists struggle to identify equations that accurately capture what is happening in the real world.

In both cases, scientists have created simplified subcomponents known as parameterizations to approximate these physical processes in the model. A major goal of LEAP is to improve on these parameterizations with the help of machine learning, which can leverage the incredible wealth of Earth system observations and high-resolution model data that has become available.

By training the machine learning model on these data sets, and then collaborating with Earth system modelers to incorporate these subcomponents into CESM, the researchers expect to improve the accuracy and detail of the resulting simulations.

Our goal is to harness data from observations and simulations to better represent the underlying physics, chemistry, and biology of Earths climate system, said Galen McKinley, a professor of earth and environmental sciences at Columbia. More accurate models will help give us a clearer vision of the future.

To learn more, read the NSF announcement and the Columbia news release.

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NCAR will collaborate on new initiative to integrate AI with climate modeling | NCAR & UCAR News - UCAR

AI is everywhere in tech, and you can bet it will be a vital topic at TechCrunch Disrupt 2021 on September 21-23. Its the driving force behind just about everything from rockets, robotics and customer service to deep science and autonomous vehicles. Its even changing the game for dystopian fiction writers.

As always, every Disrupt features peerless experts and boundary-pushing visionaries, and Disrupt 2021 will not disappoint on that or any other score. With more than 80 interviews, panel discussions, events and breakout sessions and counting were shining a spotlight on sessions related to AI and robotics.

Get amongst it: Buy your pass today, join thousands of attendees from around the world and get ready to learn about the latest in AI, robotics, rockets and so much more.

Here are just some of the AI and robotics presentations we have on tap. Youll find the specific days and times listed in the Disrupt 2021 agenda.

A Fictional Future Built with Real AI

A leading mind in AI research and investment and a bold new voice in science fiction collaborate in AI 2041, a remarkable new collection of stories imagining a future shaped by technology being built today. Hear Kai-Fu Lee, Sinovation Ventures chairman and CEO, and author Chen Qiufan (AKA Stanley Chan) discuss the tech that inspired their book and the changes they expect over the next two decades.

Drones, Self-driving Cars and Everything in Between

Pete Buttigieg first came on the scene as a small-town mayor in Indiana. He launched onto the national stage as a presidential candidate for the Democratic party in 2020. He now serves as Secretary of Transportation under the Biden administration and oversees everything from public transport to autonomous vehicle regulation. Hear Secretary Buttigiegs take on micromobility, the future of cities, drone delivery, autonomous vehicles and more in this fireside chat.

Crafting a Lunar Trajectory in Newspace

Rocket Lab has upgraded its ambitions from building a global launch empire to designing its own spacecraft and visiting the moon and beyond. Founder and CEO Peter Beck will speak to the challenges and opportunities lying ahead for his fast-growing space and tech outfit.

Demo Derby: How startups are disrupting the status quo with innovative data analytics, AI and modern app development

Startups need to move quickly and focus limited resources on areas where they can differentiate. In this fast-paced session, learn from startups and Google Cloud experts like Dave Elliott, developer advocacy lead, AI how you can leverage Google technologies to serve customers better and get to market more quickly. In a series of short demos, see how innovative startups and Google experts have used Google compute, storage, networking and AI technologies to disrupt the status quo. Presented by Google Cloud.

Humanizing AI: How Brands Are Revolutionizing Customer Experience in an Increasingly Digital World

Empathy deficit is the largest imminent threat to a businesses growth, but theres hope. Humanized AI is allowing brands to create empathetic customer experiences by offering uniquely personal interactions with digital people. But what is empathy, really? And how can it help brands and storytellers better connect with their audiences in a cookie-less world? Soul Machines co-founder and CBO Greg Cross explains how embracing AI could be just the competitive advantage that your brand needs. Presented by Soul Machines.

The New Human and Planetary Health Pioneers

Mammoth Biosciences, co-founded by Nobel Laureate Jennifer Doudna and Trevor Martin, is the industrys first CRISPR platform company. It has already delivered a breakthrough COVID test and has inked partnerships for novel CRISPR diagnostics, therapeutics and biomanufacturing with leading healthcare companies. NotCo, founded by Matias Muchnick, is combining artificial intelligence and deep science to re-invent the food industry, starting with a milk alternative product, with many more to come. Hear about the founder journey from these breakout companies and tips for scaling your business. Presented by Mayfield.

So You Want to Build a Space Business?

The space industry is changing faster than ever, with new technologies and lower launch costs democratizing access to space and driving a new era of innovation. The opportunities to build the next great business are seemingly endless, but space can be a difficult and unforgiving place, especially for those new to the domain. This session will feature practical insights and advice from experienced space leaders for entrepreneurs looking to get their business off the ground.

Korea Pavilion Pitch Session

We present the 13 pioneering Korean companies that will enrich our lives with their innovative edge. The companies specialize in various technologies, including Green Tech, AR/VR, 3D Display, AI & Big Data, and Cybersecurity. Dont miss your chance to catch a glimpse of ingenuity from the technology powerhouse. Presented by KOTRA.

TechCrunch Disrupt 2021 takes place on September 21-23. Buy your pass today and learn about the latest trends and developments in AI and robotics.

Is your company interested in sponsoring or exhibiting at Disrupt 2021? Contact our sponsorship sales team byfilling out this form.

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Everything AI and robotics at TechCrunch Disrupt 2021 - TechCrunch

Given that artificial intelligence is increasingly overtaking people on a range of expert tasks, will it someday make human managers obsolete? Luckily, theres one cognitive ability where people still have a big edge: reframing. Reframing is not about solving a problem (with either intuition or conscious reasoning) but about defining what exactly is the problem to be solved. It isnt easy, and its usually time-consuming, but it is key to both discovering breakthrough innovations and adapting to a rapidly changing environment. Four tactics can help you cultivate this ability: dedicating time to not thinking about the problem, making hidden assumptions explicit, playful exploration, and leveraging surprising analogies.

Of all the tools managers use to lead their businesses, thinking is the most crucial. It involves two distinct ways of processing information: intuitive and conscious, which the Nobel laureate Daniel Kahneman labeled thinking fast and slow. Today computers increasingly outperform people in both. With their raw calculative power, computers easily beat humans in conscious-reasoning tasks, as long as the rules and parameters of the situation are known. Managers routinely turn to mathematical optimization and simulation to build investment portfolios, make pricing decisions, and understand supply-chain risks. And while humans used to be superior at pattern recognition, which is largely intuitive, computers now can be trained to develop their own intuitions from large masses of data using machine learning. In recent studies they proved better than humans at expert tasks such as detecting cancer in computer tomography scans and choosing investment targets.

Given the way things are going, can managers continue to add value to organizations? Luckily, theres one cognitive ability where people still have the edge over computers: thinking really slow.

Really slow thinking is used in reframing the process by which we reexamine the parameters, objectives, and assumptions we approach decisions with. Reframing is not about solving the problem (with either intuition or conscious reasoning) but about defining what exactly is the problem to be solved.

Reframing isnt easy. The way in which managers frame decisions can be deeply entrenched in industry traditions, organizational history, and executives own education and experience. Reframing can be extremely time-consuming, which is why we see it as thinking really slow.

Reframing is crucial because groundbreaking business model innovations often result when companies break away from established ideas about how value is created and captured. Look at Amazon. In 1999 a CNBC reporter challenged Jeff Bezos because the company, with its large, expensive distributions centers and many employees, was no longer the pure internet play investors were high on. Internet, shminternet, Bezos replied. He rejected the view that a low-cost online business model was essential to competing. Instead of accepting the pure internet versus traditional retail dichotomy, he reframed the conversation in terms of an obsession with delivering a great customer experience and explained how all Amazons strategic choices focused on that goal.

When market dynamics change, reframing can be especially critical. Consider Nokia. In the feature phone business, it had learned to expect that with successful new offerings, sales took off quickly and profits were good. As a result, the company decided against some costly investments and walked back courses of action that didnt produce immediate results. In the early 2000s it pulled the plug on many pioneering innovations that were seen as too risky or didnt initially experience widespread adoption, including touchscreen phones, tablet devices, and mobile gaming. This approach was particularly damaging when competition moved to the ecosystem level. While Nokia continued to flood the market with new hardware, software development kits and third-party ecosystem and apps were a second priority, a former Nokia executive lamented. Moreover, as a former Nokia manager put it in an interview, Large-scale consumer services are not made in a year or two. We have often lacked patience for that. The smartphone era required a new long-game mindset that the quickly moving hardware king lacked.

Humans ability to think really slow also is key to state-of-the-art AI, which doesnt function unless people first reframe a business problem as an AI problem. As Ajay Agrawal, Joshua Gans, and Avi Goldfarb have argued, AI is simply a variety of prediction algorithms. Reframing problems that demand time-consuming human judgment and careful analysis (such as identifying insurance fraud and assessing creditworthiness) as prediction problems is precisely how the likes of Lemonade and Kabbage have shaken up mature businesses such as consumer insurance and small-business lending.

In a world where managers can use computers to enhance their ability to think fast and slow, the ability to reframe will increasingly separate the wheat from the chaff. Here are four strategies to help you cultivate it:

Dedicate time to not thinking about the problem. Research suggests that a period of incubation helps produce more creative solutions. When you set aside a problem for a period, you distance yourself from its current framing, making room for restructuring and spontaneous insights. So after you initiate the process of solving a problem, go and do something completely different for a while, letting it cook slowly on your back burner.

Make hidden assumptions explicit. Were mostly unaware of the limiting, self-imposed assumptions with which we approach situations. Group processes that are designed to induce cognitive conflict can help surface them. You can make one group argue against another groups solution (devils advocacy) or make two groups develop opposing solutions to a problem (dialectical inquiry). Building a mathematical model of the problem can also be helpful, because it forces you to spell out assumptions about what is causing the problem and how proposed remedies are supposed to work. Modeling often reveals unanticipated dynamics, triggering shifts in mindsets about how to best manage certain things. When Fluor Corporation introduced simulation modeling to help predict changes in the costs and schedules of complex projects, managers started to see that those changes could be managed proactively rather than dealt with the retrospectively, as was industry practice at the time.

Engage in playful exploration. Injecting an element of the imaginative into decision making can help managers mentally distance themselves from tacit assumptions and industry recipes what everyone who knows the industry understands and unleash creativity. This liberation from ordinary constraints can be accomplished by, for example, asking teams to build Lego models of their business ideas in order to communicate them to others.

Leverage (surprising) analogies. Analogies are powerful tools for reframing familiar problems. Ideas and practices from one industry can be used to reshape another. Berry Gordy Jr., for instance, made Motown Records into a hit factory by modeling it after the Ford Motor Companys assembly line, where he had previously worked. In some cases, exposing yourself to something completely different like combat sports, opera, or superhero comics can be a great way to gain fresh insights that other insiders lack. Apples minimalist design, for instance, was inspired by the calligraphy classes, Zen Buddhism lessons, and Bauhaus architecture Steve Jobs was exposed to. Even when the analogy is imperfect, it may provide the rough outlines of a novel framing of a vexing problem.

While managers can add these practices to their tool kits to enhance their own reframing capabilities, they also have a responsibility to ensure that the broader organization supports reframing. The first step is to build channels and foster a culture where the in-house devils advocates and visionaries can voice their concerns and ideas and employees have time for playful exploration and incubation. Though such efforts may not result in tangible benefits immediately, they may be essential for the renewal and long-term prosperity of the organization and its stakeholders.

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Why AI Will Never Replace Managers - Harvard Business Review

The Transform Technology Summits start October 13th with Low-Code/No Code: Enabling Enterprise Agility. Register now!

This article was written by Bob Friday, Vice President and Chief Technology Officer of Junipers AI-Driven Enterprise Business.

In a recent survey of 700 IT pros around the world, 95% said they believe their companies would benefit from embedding artificial intelligence (AI) into daily operations, products, and services, and 88% want to use AI as much as possible. When was the last time you heard that many people agree on anything?

Yes, AI is all the rage because it is the next step in the evolution of automation in doing tasks on par with human domain experts whether it is driving a car or helping doctors diagnose disease. But make no mistake while we are starting to see the fruits of AI here and there: By and large, the industry and most organizations are still in the early days of AI adoption. And as with any new momentous technology, organizations need to develop an adoption strategy specific to their organization to get the full benefits of AI automation and deep learning technology.

The complication as Gartner put it: How to make AI a core IT competency still eludes most organizations.

But failing to learn how to leverage the benefits AI/ML will leave an organization at a competitive disadvantage in terms of customer experience and operational efficiency. So, whats the way to get there? Here are three common traps that companies should steer clear of as they get themselves AI-ready.

Great wine requires good grapes and great AI starts with good data, but great AI also needs a clear business ROI. The business benefit ROI and the data needed to automate the domain expert task must be clearly defined at the outset of the project if the AI solution is to deliver real value and continue receiving the resources to grow from pilot to production.

AI ingredients, like algorithms and machine learning, sound very science-y, but business AI projects should never resemble science experiments. The Shiny New Toy Syndrome is a real pitfall for AI. To avoid succumbing to it, organizations should tie every AI project to specific business outcomes and know the business outcome question and what task you are trying to do on par with a domain expert.

For example, is the objective of using intelligent automation to relieve IT team members of mundane, routine tasks so they can focus on higher-value activities? Beyond the IT department, is it to help the marketing department gain competitive advantage by delivering more personalized experiences to customers? Is it automating more of the sales process to boost lead volume and close rate?

C-suite leaders would have to be living under a rock at this point not to recognize AIs potential and the fact that investment is required for AI-ready technology stacks, but theyre going to want to understand how its good for the business. Everyone in a company needs to recognize this reality, and ward off any squishiness in an AI projects reason for being.

The AI talent shortage is often cited as one of the tech industrys toughest challenges. It has even been called a national security threat amid Chinas ambitions to become the world leader in AI.

According to OReillys 2021 AI Adoption in the Enterprise report, which surveyed more than 3,500 business leaders, a lack of skilled people and difficulty hiring tops the list of AI challenges.

To make sure their companies have the talent to fully leverage the benefits of AI/ML they should start both a hiring and training program.

On the hiring side, companies should look for talent beyond the typical data science degree and look at adjacent degrees such as physics, math and self-taught computer science. But hiring talent is not enough for a companies strategy to build their AI workforces, especially when theyre competing with behemoths like Amazon and Facebook. Another good solution to consider: If you cant hire them, train them.

While its unreasonable to expect someone to become a data scientist after taking a couple of online Coursera classes. Engineers with Physics, Math and Computer Science backgrounds have the foundation to master data science and deep learning.

Sources of talent may exist inside the organization in unexpected places. Take, for example, the large business intelligence (BI) ecosystems that many companies have. These have talent that is familiar with using Bayesian statistical analysis that is common to most machine learning algorithms.

In making sure they have the right skills to support AI initiatives, it makes sense for companies to re-train existing employees as much as possible in addition to having an AI/ML hiring strategy. Companies need to get creative in pinpointing those employees and AI/ML talent.

Ive seen companies get bogged down by trying to build their own AI tools and solutions from scratch rather than buying them or leveraging open source. The algorithms being used to develop AI solutions are fast evolving and companies should look to partner with vendors in their industry who are leading the AI wave. Unless it happens to be one of the companys core competencies, building AI solutions is usually an overreach. Why reinvent the wheel when you can buy one of the many commercial AI tools on the market?

Deloittes most recent State of the AI in the Enterprise report, which surveyed 2,737 IT and line-of-business executives worldwide, found that seasoned and skilled AI adopters are more likely than starters to buy the AI systems they need.

This suggests that many organizations may go through a period of internal learning and experimentation before they know whats necessary and then seek it from the market, the report said.

Companies that avoid these three traps will have a much easier time accelerating their AI adoption and enjoying the benefits of revenue growth, lower operating costs, and improved customer experience.

Bob Friday is Vice President and Chief Technology Officer of Junipers AI-Driven Enterprise Business.

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3 traps companies should avoid in their AI journeys - VentureBeat

AV8 Ventures unveiled its AV8 Ventures II fund with $180 million from Allianz Group, an insurance and asset management giant, aimed at supporting entrepreneurs developing artificial intelligence-driven technologies in the areas of health, mobility, enterprise and deep tech.

Since the Palo Alto venture firms launch in 2018, it has invested in 20 seed-stage companies, with another four in the pipeline. Its first fund was also $180 million and backed by Allianz, George Ugras, managing director at AV8, told TechCrunch. The new fund will also invest in seed stage and some Series A and will aim to go into 25 companies.

The idea is to operate as a financial VC with the support of the world largest insurance company and asset manager behind us, Ugras said.

Some of the technologies the firm is excited about include how chronic diseases are managed. Ugras believes the lack of access to swaths of data and alignment of interest around the table are prohibiting many of the right solutions from bubbling up. In enterprise, AV8 is looking at management around cyberattacks, predicting vulnerabilities and the impact they have on enterprises, so that companies can be proactive in securing their vulnerabilities versus reactive.

Meanwhile, the driver for the second fund was to ensure continuity in deal activity. AV8 is seeing so many deals right now, and the competition to get into a VC deal makes it difficult to project how fast a fund will be able to deploy the capital. Even if a firm gets excited and issues terms sheets, there is always uncertainty, he added.

With venture capital being abundant these days, Ugras noted that the velocity is the fastest he has seen in 22 years. The competitiveness in the market is such that if a startup has a decent team, there is no issue raising capital. However, on the investor side, they have to do things better than ever.

In terms of the key diligence, you need domain expertise to be very clear on how you can add value and key execution milestones going forward, he added. Healthcare and insurance more so than others because the business models are complicated. If you have the startups educating you on the front end, it is going to be difficult for the fund.

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Allianz backs AV8 Ventures second fund focused on AI technologies - TechCrunch