Sorites de produit, innovation, start-up
Artificial General Intelligence (AGI) should be able to see. In particular, it should be able to recognize objects and to learn to recognize new classes of objects from as few examples as possible.
This means that it should generalize. How would that work?
There is no denying the fact that we are more connected than ever today and this connectivity only seems to increase by the day. The world today has shrunk within a small handheld mobile device, hasn’t it? Smarter technology is bringing not only the world but the future closer.
Alongside, this trend has exponentially increased the rate of data generation. Servers are not the only high-volume data-sources anymore. Mobile devices and internet of things (IoT) are churning out a copious amount of information each second. As the number of smartphones and connected devices grows, this inflow of data multiplies too. It should be noted that this data is multiplying with each second and getting more and more massive in size.
The adoption of virtual assistants is trending greatly. It’s highly impossible to overlook the AI-controlled assistants being used in gadgets. From arranging days, booking, and ordering, technology has transformed to showcase amazing progress. The trend concedes to be in its initial phase and requires time to figure out the facts from the hype to understand the exact worthiness of the technology.
Some of the most popular AI assistants racing for attention: 1) Amazon (Alexa), 2) Apple (Siri), 3) Google (Google Assistant) and 4) Microsoft (Cortana).
The hype around the use of artificial intelligence in decision-making might make you think it could pilot your company automatically, talk to your suppliers, chase late invoices, and open parcels arriving in the mail room, all while making you a nice cup of tea.
In practice, AI is a precision tool that should be used judiciously to achieve specific goals. Implementing it takes foresight and a vision, along with a healthy understanding of the technical challenges involved.
The idea of model fusions is pretty simple. You combine the predictions of a bunch of separate classifiers into a single, uber-classifier prediction, in theory, better than the predictions of its individual constituents.
As my colleague Teresa Álverez mentioned in a previous post, however, this doesn’t typically lead to big gains in performance. We’re typically talking 5-10% improvements even in the best case. In many cases, OptiML will find something as good or better than any combination you could try by hand.
An insurance claim is "a formal request to an insurance company for coverage or compensation for a covered loss or policy event" (source: www.investopedia.com). Once initiated, the claim often goes through a complex process with one of two possible outcomes — the claim is either accepted, leading to a settlement, or rejected. The claims process would typically be: contact the insurance company, start of the claimant investigation, check the policy coverage, evaluate the damage and arrange compensation payment.
I recently built a Twitter Bot that plays Blackjack. Here are my previous posts so far if you are catching up:
Since interaction with the Bot is via Twitter, there will be an unknown length of time between when a player chooses to reply and interact with the bot, which could be seconds, minutes, or days. Therefore, we need to store the gameplay state for each player, retrieve it on the next interaction from the user, and store it again after the response from the bot while we wait for the next player interaction.
A couple of weeks ago, we released a new capability in Parasoft SOAtest called the Smart API Test Generator. I was geeked. This technology is legitimately groundbreaking — it uses artificial intelligence to convert manual UI tests into automated API tests so you don’t need expertise in API testing or even the ability to write any code at all to get started. It’s all script-less, and it’s activated through a simple plugin for Chrome, so you don’t have to install a large toolset in order to use it.
However, at the STAREAST testing conference back in May, where I gave a long talk about how awesome this technology is, I kept encountering people ask me how this was different from record and replay technologies that already exist on the market. Of course artificial intelligence is the answer, but AI for AI’s sake is meaningless — why do we even care?
As a developer, how difficult is it to choose between innovating in an untested market vs. going with a safe rehash in a tried-and-true market?
This is the question of the hour these days, with so many players up in a frenzy over lack of industry innovation in the AAA market. So many developers are honestly just trying to put something out there that can generate income while simultaneously entertaining the masses.
"Most people are not yet looking at graph databases from a machine-learning point of view. All the inherent knowledge we as humans use to make decisions can be encoded in a graph structure," said Ajinkya Kale, Senior Applied Researcher at eBay’s New Product Development Group.
For Ajinkya, it’s all about the synergy between graphs and machine learning. As a Senior Applied Researcher at eBay’s New Product Development Group, Ajinkya and his team use Natural Language Understanding to bake machine learning into the graph database that drives eBay’s virtual shopping assistant, eBay ShopBot.
