Defines, Designs, And Delivers Technology-Enabled Business Solutions
Data Retention Policy And Its Importance To Your Organisation
Branding Authenticity Is Excellent, But It Must Be Supported By A Great Product
These Five Strategies May Help Educators Build A Professional Brand And Improve Their Online Presence
According To A Research, Over 70% Of Organisations’ Apps Are Disconnected From One Other And The Core Business
AI Leaders Need To Know Both The Opportunities And Dangers That Come With Artificial Intelligence And Algorithms
The Risks Of Including Everyone In AI Decisions In Your Organization
If You Feel Your Creative Potential Is Being Overlooked, It’s Time To Rebrand Yourself
Philanthropies: Are We Utilizing Our Resources And Skills To Reach Our Goal And Benefit The Most Marginalized?
Why Use Strong Passwords? Everyone Knows This, But Too Few People Actually Follow It
Instead Of Replacing Them, Businesses Are Merging Mainframe Systems With New Technologies To Expand Their Capabilities
If there’s one thing I’ve learned over the 15 years working on Google Search, it’s that people’s curiosity is endless. We see billions of searches every day, and 15 percent of those queries are ones we haven’t seen before – so we’ve built ways to return results for queries we can’t anticipate.
Manufacturing CEOs I’ve spoken with are in unanimous agreement that the best way to drive new revenue growth is by transitioning to more services-based revenue models based on next-generation products. Their product roadmaps include configurable, customized products capable of delivering data back to manufacturers they can monetize as services. Manufacturers are looking to get beyond relying on transaction revenue alone. They’re most focused on how they can use configurable products to launch higher-margin outcome-based business services, and their 2020 roadmaps reflect this goal. The following graphic from McKinsey’s Leveraging Industrial Software Stack Advancement For Digital Transformation (50 pp., PDF, no opt-in) explains why manufacturers’ 2020 roadmaps are dominated by more configurable products capable of driving new services-based business models.
Tom Siebel’s reputation in business has been cemented as a pioneer in relational databases and as a godfather of customer relationship management. The latter came to fruition through his eponymously named Siebel Systems, which he founded in 1993 and sold to Oracle in 2006 for $5.8 billion.
In 2009, Siebel founded the enterprise artificial intelligence platform company C3.ai, which provides applications for multiple commercial uses, including energy management, predictive maintenance, fraud detection, anti-money laundering, inventory optimization, and predictive CRM.
Artificial Intelligence (AI) as a market is full of hype, with vendors, customers, and press all speaking breathlessly about the capabilities for AI in general and their offerings specifically. Likewise, blockchain is also a widely hyped market, with technology providers and customers claiming all sorts of capabilities that may or may not be possible. Combining AI and blockchain then must be double the hype? On the other hand, AI is providing real, tangible value in many myriad ways we talk about every day. Likewise, blockchain is starting to show value across a range of applications and industry. So, perhaps combining AI and blockchain will also show twice the value combined together.
Deep Learning (DL) has enabled significant progress in computer perception, natural language understanding, and control. Almost all these successes rely on supervised learning, where the machine is required to predict human-provided annotations, or model-free reinforcement learning, where the machine learns policies that maximize rewards. Supervised learning paradigms have been extremely successful for an increasingly large number of practical applications such as medical image analysis, autonomous driving, virtual assistants, information filtering, ranking, search and retrieval, language translation, and many more.
Sci-fi and science can’t seem to agree on the way we should think about artificial intelligence. Sci-fi wants to portray artificial intelligence agents as thinking machines, while businesses today use artificial intelligence for more mundane tasks like filling out forms with robotic process automation or driving your car. When interacting with these artificial intelligence interfaces at our current level of AI technology, our human inclination is to treat them like vending machines, rather than to treat them like a person. Why? Because thinking of AI like a person (anthropomorphizing) leads to immediate disappointment. Today’s AI is very narrow, and so straying across the invisible line between what these systems can and can’t do leads to generic responses like “I don’t understand that” or “I can’t do that yet”. Although the technology is extremely cool, it just doesn’t think in the way that you or I think of as thinking.
Let’s look at how that “thinking” process works, and examine how there are different kinds of thinking going on inside AI systems.
17 years ago ex-Microsoft CEO Steve Ballmer called Linux a cancer. That statement still casts clouds of doubt over the company and a lingering anxiety among Linux and open source enthusiasts. But hey, Ballmer loves Linux now! So does Microsoft!
Whether or not one chooses to believe it, Microsoft continues to bang that drum with both actions and words; most recently at the 2019 Red Hat Forum in Australia.
Microsoft Australia CTO Lee Hickin addressed the attendees with a message about the company’s transformation and its continued commitment to open source.
In attendance was ZDNet, which captured some key quotes: “We’re in an amazing place right now with a leader like Satya who really understands what it means to think about where we need to be for our customers, to really transform the company from being essentially the proprietary software company, to being an open source company,” he said.
Companies who bring many of these customer service trends to life are the ones who are going to be successful. Nike is one of those companies. They invested in a value-added app that helps customers track their runs. At the same time, the app provides value to the customer, it’s providing information about customers to Nike. So, after a certain amount of miles run, the app might suggest to the customer it’s time for a new pair of Nikes. Through augmented reality, the customer can point the app to their foot and try on different styles. Then, it’s able to size your foot perfectly, and you can place the order and get your new trainers in record time. And for those that prefer a tangible experience, you can touch, feel, and smell their products in their flagship stores.
The systems that have prevailed in the past are not working. They have not served their citizens well. We have lost trust in these systems. If we are to create a future free of censorship and surveillance, we have a massive undertaking that awaits us. I have always believed that the function of the society was to set up the necessary systems to help individuals live good lives where they have an opportunity to achieve their dreams. This has been achieved through centralized solutions.
Governments throughout history have always bent technology to their advantage. Today, nation states have built staggering surveillance systems, and continue to exert more control over their citizens. Can decentralized networks like bitcoin help strengthen civil liberties and challenge this consolidation of power? HRF’s Alex Gladstein provides a tour of how technology is being used against us, and shows how it just might be the one thing that can save our privacy and freedom.
AI bias is difficult to overcome. But it is a joint effort of corporations, researchers, lawmakers, and the media. When many eyes are on the issues, we may have a lot more data, opinions about the data, and judgments passed, but with “common sense,” we come closer to equality, to human kindness, and to the protection of our constitutional liberties. That to me is an opportunity to exercise our democratic process.
Unstructured data can’t be easily stored in a traditional column-row database or spreadsheet like a Microsoft Excel table. It’s therefore more difficult to analyse and not easily searchable, which is why it wasn’t useful for organizations until recent years. Today, however, we have unstructured data analytics tools powered by artificial intelligence (AI) that were created specifically to access the insights available from unstructured data.
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