Enterprise big data and analytics cuts through the hype to make sense of data collection, storage, management, dissemination and discovery technologies, all employed collectively as a means of realizing corporate efficiencies and uncovering business opportunities.
• There’s a race right now in high tech to build the first general purpose quantum computer, with industry leaders IBM, Google, D-Wave Technologies, and Intel each building out very different implementations of a single, revolutionary idea — the use of qubits instead of plain old bits.
• But unlike most races, this one has no clear finish line as we’re still figuring out the best approach to quantum computing or to building software for them. Enter IT services powerhouse Atos, which is backing a pure but as yet simulated idea of quantum computing in an effort to garner what matters most, namely the hearts and minds of future quantum developers.
There’s an awful lot of noise in the technology industry right now regarding the promise of quantum computing. A sizable number of dissimilar technology and platform players, ranging from Intel to Google to Atom Computing (a 2018 startup) are all busy building increasingly capable computers that push and pull qubits rather than bits. And as you might expect from such a diverse cast, there are a lot of differing views on how to build such a beast and how to best put it to use. Continue reading “Atos Has a Secret Weapon, and It Rhymes with Awesome Computing”→
• During IBM Think, IBM made several AI-related announcements, some designed for enterprises with complex requirements, and others geared towards helping businesses deploy their first AI solution.
• Although IBM’s new capabilities and tools in support of deep learning are impressive, and position IBM as a thought leader, it’s the steps IBM is taking to help companies just getting started with AI that truly move the market forward.
IBM Think was promoted as an event that would bring together the greatest minds in AI. It featured technologies such as virtual assistants, machine learning (ML), and deep learning (DL), and also touched on hot button issues such as ethics and AI. During her keynote, CEO Ginni Rometty discussed the transformational role that AI will have on the IT market going forward, and she introduced Watson’s Law, describing it as a follow on to Moore’s Law and Metcalfe’s Law. Continue reading “IBM Think 2018: Big Blue Looks to Help Companies Adopt Their First AI Project”→
• When it comes to swapping ones and zeros, quantum computing promises to outpace traditional processors in pure scale.
• Yet its true promise will play out when we learn how to invoke quantum phenomena in order to speed up artificial intelligence (AI).
At last week’s IBM Think conference in Las Vegas, Big Blue and AI chip manufacturer NVIDIA talked up the importance of hardware in resolving AI performance bottlenecks. As it turns out, building a smart AI system demands not only copious amounts of data but also the ability to rapidly run machine learning (ML) and deep learning (DL) algorithms against that data. The trouble is that quite often hardware gets in the way. Continue reading “This is Your Brain on Quantum Computing”→
Domo remains as flamboyant as ever both in how it goes to market and in how it approaches BI as a business operating system.
Yet, a surprising new go-to-market message hints at a newfound maturity that underscores the company’s desire to play a crucial, central role in the success of its customers.
To say that the corporate culture at Domo is unique is to do a serious disservice to all Domo employees, or ‘Domosapiens,’ as they like to call themselves. Domo’s corporate culture is not your typical corporate attempt to feign a sense of style. Domo is downright wacky behind the leadership of its enigmatic founder and CEO, Josh James. Case in point, at this year’s Domopalooza conference in Salt Lake City, Mr. James made a rather interesting entrance during the keynote. Not content to follow the opening entertainment act, put on by the KinJaz dance group, the Domo CEO actually danced a full routine with the group. Continue reading “Take Two Domo and Call Me in the Morning”→
Businesses looking to adopt AI must not only evaluate the technology’s implications on job displacement and data security, but also consider that algorithms may unintentionally undermine the organization’s ethical standards.
Customers are quick to pass judgement; if unintentional biases become public, a company’s brand reputation may suffer significantly.
Much has been written about ethics and artificial intelligence (AI), and rightly so. With many organizations looking to adopt some form of AI technology in 2018, business leaders are wise to stay on top of emerging ethical concerns.
Job displacement is still a key consideration, as is safeguarding data. In a recent GlobalData survey, 23% of organizations indicated they had cut or not replaced employees because of AI; 57% indicated security as a top concern.
However, looking ahead, the question of ethics is the real challenge the AI community will need to tackle. And it is a challenge that is far more controversial than security or privacy. What happens when a self-driven car needs to decide between hitting a child that has run into the road, or swerving and risking the injury of its passenger? How proactive should a personal assistant be when it detects wrongdoing? What should be done when a personal assistant believes that a user’s usage pattern points to having committed a serious offense – should it alert authorities?
Probably more relevant to business leaders is the concern that they may not know if an AI infused application will perform up to their organization’s ethical standards. It may contain unintentional racial bias – say a financial algorithm that is biased against a specific race, or an application that demonstrates a preference towards one gender over another. What should be done when a phrase that is acceptable when said by one demographic is completely unacceptable when uttered by another – can an algorithm be trained to reliably make this distinction? Maybe, but what happens when it makes a mistake?
On the one hand, unintentional results are not the fault of the organization using the AI solution. The responsibility may lie in the data used to train the underlying machine learning model. However, customers are quick to pass judgement. If and when these unintentional biases become public, customers will quickly assign blame to the company using them, potentially with enormous impact to a brand’s reputation.
Just as CEOs may take the blame for customer data breaches, and as a result may lose their jobs, senior leaders are also at risk of taking the fall when an AI solution implemented by their organization crosses an ethical line. It’s in their best interest to ensure that doesn’t happen – their reputation depends on it.
The Internet of Things is not only changing how consumers interact with the world around them; it is also driving a tectonic shift in how companies process and analyze device data.
Traditional best practices for gathering and analyzing data, where information is stored and processed centrally, are no longer relevant. Forget big data warehouses. IoT customers are looking to analyze data as close to the source as possible, at the edge of the network.
The announced IBM and Unity partnership has the potential to expose a larger audience to the world of AI.
The implications of the deal go beyond gaming; it could change both the way consumers expect to interact with software – at home and at work – and the way developers design software in the future.
Last week, Unity and IBM announced a partnership that could have significant implications for the adoption of artificial intelligence (AI) in both consumer and business applications. The two companies launched IBM Watson Unity SDK, which enables developers to integrate Watson’s cloud-based AI features into Unity applications. Developers can include features such as Watson’s speech-to-text, visual recognition, language translation, and language classification capabilities in their programs, changing how users issue commands and how software responds to them. Continue reading “The Unity and IBM Partnership Is as Much About Business Apps as It Is About Gaming”→
Internet of Things (IoT) adoption is certainly being driven by the promise of real-time analytics and AI at scale, but its ultimate feasibility still depends on something much more mundane, namely how efficiently it can move data between connected devices and backend systems.
And yet, according to a recent GlobalData study, IoT practitioners haven’t yet learned that lesson, relying not on fit-for-purpose protocols like MQTT, but instead on the ubiquitous, now aging web standard, HTTP.
At Mobile World Congress this week, networking giant Cisco rolled out a new networking and device management platform for IoT practitioners that promises to enable the creation of extremely large-scale deployments without breaking the bank. IoT at scale is a no-brainer. More devices equal more data. More data equals deeper business insights. But, IoT at scale can be expensive in terms of delivering basic device interconnectivity and management costs. Continue reading “The Internet of Things Isn’t Driven by Devices as Much as by the Internet Itself”→
• Many organizations are unsure of how to best incorporate AI to meet their industry-specific challenges – often because the use case options are so vast and so varied.
• Organizations – particularly mid-sized businesses, companies starting out on their analytics journeys, or those rolling out IoT solutions – should explore the services available from their telecom provider, many of which have built out their professional services capabilities around digital transformation.
• With big data and analytics, older ideas like predictive analytics and AI are coming together to solve long-standing problems, most notably data quality.
• Sisense is adding another twist by taking advanced design and visualization concepts and putting those to work at the very beginning of the analytics lifecycle.
Invention invariably involves theft. Each generation of inventors stands on the shoulders of its predecessors, borrowing freely from their available pool of knowledge. Ideas are deconstructed, mixed up, and reapplied in new ways and within unexpected contexts to form, well, something new. Sometimes these new inventions are simply the opportunistic reinterpretation of an existing idea, taking something unique but impractical and turning it into something incredibly useful. That’s the way it was with the invention of the automobile, the light bulb and the radio. And that’s how it is with big data and analytics, where older ideas are only now coming together to solve long-standing problems. Continue reading “To Improve Data Quality, Sometimes the Best Place to Start is at the Very End”→