If you are looking for an answer to the question What is Artificial Intelligence? and you only have a minute, then here's the definition the Association for the Advancement of Artificial Intelligence offers on its home page: "the scientific understanding of the mechanisms underlying thought and intelligent behavior and their embodiment in machines."
However, if you are fortunate enough to have more than a minute, then please get ready to embark upon an exciting journey exploring AI (but beware, it could last a lifetime) …
Facing globalization, increased product complexity, and heightened customer demands, companies are taking up advanced technologies to transform their supply chain from a pure operations hub into the epicenter of business innovation. Using sensors and ever-improving internet connectivity, forward-thinking companies are collecting data at every checkpoint, from the status of raw materials flow to the condition and location of finished goods. Machine learning, artificial intelligence (AI), and advanced analytics help drive automation and deliver insights that promote efficiencies -- making on-the-fly route changes to accelerate product delivery, for example, or swapping out materials to take advantage of better pricing or availability. Additive manufacturing is also opening doors to easy production of spare parts, enabling companies to slash inventory, cut costs, and create supplementary revenue streams. These advanced technologies are serving as a springboard for new business models -- for example, many firms are piggybacking off the "internet of things" (IoT) to offer predictive maintenance services that guarantee product uptime while generating recurring revenue.
Transformation is defined by a change in state over a period of time. This is often an ongoing, natural process. For example, as humans we spend our entire lives constantly transforming, both physically and mentally, whether that's growing taller and stronger, or developing emotional intelligence. But sometimes transformation is a necessity, often resulting from the need to adapt to new circumstances. However, regardless of circumstance, transformation can never be instantaneous, and digital transformation for businesses is no exception.
Logistics and supply chain management is one such industry that plays an important role in our day-to-day lives. It helps us in getting our couriers, baggage, shipments, etc. delivered on time. Logistics and supply chain management not only help us indirect ways, but it also plays crucial roles in indirect ways. Right from the delivery of fuel to petrol bunks to supply of industrial equipment, fruits vegetables, and daily necessities, the supply chain makes its mark in our lives in multiple ways. Thus, it has turned out to be an everyday part of our lives.
Oracle OpenWorld -- Organizations that are adopting Artificial Intelligence (AI) and other emerging technologies in finance and operations are growing their annual profits 80 percent faster, according to a new study from Enterprise Strategy Group and Oracle. The global study, Emerging Technologies: The competitive edge for finance and operations, surveyed 700 finance and operations leaders across 13 countries and found that emerging technologies – AI, Internet of Things (IoT), blockchain, digital assistants – have passed the adoption tipping point, exceed expectations, and create significant competitive advantage for organizations. AI, IoT, blockchain and digital assistants are helping organizations improve accuracy, speed and insight in operations and the supply chain, and respondents expect additional business value as blockchain applications become mainstream. The vast majority of organizations have now adopted emerging technologies and early adopters (those using three or more solutions) are seeing the greatest benefit and are more likely to outperform competitors.
Pictured above is a general purpose dual RBG camera system, designed by Carnegie Mellon University researcher George Kantor and his R&D team, to collect high quality images in agricultural environments. Collected images can feed crop-specific artificial intelligence methods that extract measurements such as crop yield, maturity, or disease incidence. Generally speaking, artificial intelligence (AI) enabled technologies are infiltrating every aspect of our daily lives, from the smartphones everyone is carrying around everywhere to places where maybe AI is best left on the sidelines (have you heard about Alexa's newest integration into a connected shower head device?). As you all know, the greenhouse has not been spared from the "AI Revolution" – not in the slightest – and one area we're hearing the technology is making believers out of skeptics is in the legal cannabis space, where high profit margins and a youthful, tech-focused grower demographic creates the perfect storm for early-stage ag tech adoption. If you disagree with that statement, I invite you to spend a day next year at the massive MJBizCon show in Las Vegas, which at this point is basically a smaller, more focused CES show for cannabis producers, and then let me know if you still don't think cannabis growers are all that innovative or on the cutting edge of technology adoption.
Stemming is one of the most common data pre-processing operations we do in almost all Natural Language Processing (NLP) projects. If you're new to this space, it is possible that you don't exactly know what this is even though you have come across this word. You might also be confused between stemming and lemmatization, which are two similar operations. In this post, we'll see what exactly is stemming, with a few examples here and there. I hope I'll be able to explain this process in simple words for you.
Manufacturing products can be very expensive and a complex process for those businesses that do not have the right tools and resources to develop quality products. In the prevailing time, artificial intelligence and machine learning have become more prevalent in producing and assembling items, helping in reducing cost and time of production. In fact, 40% of all the potential value that can be created by analytics today all come from the AI and ML techniques. In totality, machine learning can account between $3.5 trillion to $5.8 trillion in the annual value -- according to Mckinsey. In the last 5 years, it has been recorded that exponential technologies can help build robust and rapid models that drive functional improvements.
JetPatch, a next-generation vulnerability remediation cloud platform, released JetPatch 4.0, which adds machine learning and intelligent workflow automation capabilities to ensure that an organization's systems are appropriately patched. This latest release minimizes the time it takes to remediate a vulnerability once it's discovered, which allows JetPatch to substantially improve organizations' security posture. "For the first time, business leaders can transform the way their company combats vulnerabilities and shift to a single predicted, governed vulnerability remediation process. Predictive Patching slashes time to remediation, transitioning vulnerability efforts from fragmented manual processes into one orchestrated and governed operation." Some 60 percent of breaches target known vulnerabilities that have a known fix, according to Ponemon Institute research.
Honda Motor Co. said Tuesday it will reorganize its vehicle development operations on April 1 in a bid to boost efficiency. The automaker will take over vehicle development functions from its Honda R&D Co. unit and absorb another unit engaged in development of machine tools and others. The move is part of Honda's efforts to streamline operations related to each process, from planning to sales. The company also said it has established Honda Mobility Solutions Co., a new company in charge of planning and operation of cutting-edge businesses such as those related to autonomous driving and robotics.
Employees toil at lines, doing the same task, repeatedly, in order to assemble a final product. A line stoppage or bottleneck can cost a fortune. What if the manufacturer could see what was going on, in real time, and fix any issues before they become real problems? Or come up with ways to make the process run smoother and more efficiently? That's the basic idea behind Drishti Technologies, a four-year-old startup cofounded by Prasad Akella, a 57-year-old Indian entrepreneur who's best known for leading the General Motors team that developed collaborative robots in the 1990s.