The last couple of years have seen a large number of organizations and developers rush towards getting familiar with Machine Learning fundamentals and coming to grips with what it takes to integrate it into their applications. While you can definitely build out your own Machine Learning platform, it is not for everyone and companies like Google are now releasing fully managed API platforms where they expose the Machine Learning platform that they have built over the years. The main value to potential users is that these companies have likely trained their Machine Learning models for years and now the best of these services can be had with a single API call. The latest offering from Google is the Cloud Natural Language API which gives developers insights into unstructured text. A REST API is available to invoke the above functionality and we are going to deep dive into the Sentiment Analysis part of the API to first understand how it works and then build out a Slack Team helper that decodes the sentiment of the text provided to it.

Business tech companies are going to get an overdose of AI marketing. Your friendly neighborhood enterprise software provider has a window into much of your corporate data. And now it wants to provide you with artificial intelligence-fueled insights in what'll equate to a barrage of characters -- Watson, Einstein, Alexa, Siri, Cortana -- a lot of jargon and with any luck some actual automated digital processes. Rest assured you may wind up with the former before getting to the latter destination. There are some things that machines are simply better at doing than humans, but humans still have plenty going for them.

Go grab a stack of papers from the "TO-DO" pile on your desk – it's OK, I know you have one – mine is full of utility statements to be filed away, drawing from my kids, and coupons for things I will probably never buy. Now find the cable TV bill from April 2016. We (humans) are remarkably good at classifying information. We can very quickly tell the difference between a cable TV bill and a water bill. We can quickly decide if something is "What we are looking for" or "Not what we are looking for".

Artificial intelligence (AI) appears to be heading its way to business systems as technologies with human-like capabilities are increasingly adopted to enhance workplace productivity. This is recently demonstrated by Salesforce, a customer relationship management (CRM) provider, which has successfully embedded an AI to the software that salespeople use. Dubbed as Salesforce Einstein, the AI can reportedly analyze tons of data drawn from business activity, correspondence, e-commerce, email, social data streams and even data from Internet of Things (Iot). It is powered by a sophisticated algorithm that is able to learn and discover data insights to inform business decisions. "Think of Einstein as the intelligence layer between the data and the actual apps," John Ball, senior vice president and general manager of content at Salesforce, told PC Magazine.

Artificial Intelligence, or AI, is all around us and we might not even realize it! As a technology that imitates human decision making, it is present in a lot of what we use every day. AI can be found in Amazon.com Facebook tells you whom you could know and want to be friends with. Cars can automatically park for you.

Since blogging that I Need an AI BS-Meter a number of people have sent me pointers to a subset of AI I loosely think of as Result Explainers -- everything from pending government regulations (EU's Global Data Protection Regulations -- GDPR) to the latest in academic research (Local Interpretable Model-agnostic Explanations -- LIME). As the authors of the EU's GDPR state, widespread adoption of AI cannot occur until vendors are able to communicate results in a "concise, intelligible and easily accessible form, using clear and plain language." This got me thinking, "What should Result Explainers look like?" Should they generate trust scores, a series of Google-Maps like directions that get you from data to results, a series of diagrams? And as my colleague Patrick at Lab41 has pointed out, "Why should we trust a Result Explainer if we don't trust AI to begin with? As you might expect there isn't one right answer. That said, recent advances in recommenders, digital assistants, user interface design and initiatives like DARPA's recently announced Explainable Artificial Intelligence (XAI) grand challenge suggest we may be on the brink of a few breakthroughs. Again, as the authors of the EU's General Data Protection Regulations note, while the resulting classifiers, models, predictors, etc. can be very powerful they also frequently confound explanation -- e.g., the output of SVMs and Gaussian processes can be difficult to render, ensemble methods hide information as a result of aggregation and averaging, neural nets create high data dimensionality, and so on. End users care a lot more about results than they do about models. Unfortunately assessing result quality takes us right back to the models, as nonparametric models are only as good as the data used to train them (along with the type of model structure and associated parameters that were selected). But these models frequently hide information. Part of the magic of AI is that it finds stuff based on features that previously may not have been well understood. Unfortunately, the features models train on are frequently unclear. Assigning labels to pre-trained models can help mitigate some of this ambiguity -- e.g., "This model was trained with over 100,000 high-res color images of cats." These labels may be misleading though, as the model may contain feature biases that are not well understood -- e.g., "the training data is dominated by images of "well-fed, indoor cats from Japan."

Oracle CTO Larry Ellison ordered himself some new business cards on stage at the company's OpenWorld conference in San Francisco on Sunday, just by having a conversation. As part of his keynote address to attendees, Ellison took the time to show off a new set of tools for creating intelligent chatbots that integrate with Oracle's software. It's aimed at making it easier for businesses to build bots that let users connect with their enterprise software, and help businesses connect with consumers. Chatbots are a hot topic in the tech industry, with companies like Facebook, Microsoft and Slack all building tools that companies can use to create intelligent, automated conversation partners. Their growing popularity comes down to a few factors, including the proliferation of smartphones, fast internet connections and messaging apps.

Vacations are supposed to reduce stress, not create it. Just planning a vacation can be pretty darn stressful. Now, Google wants to remove all those vacation anxieties--or at least some of them. Today, the company launched Google Trips, a new mobile app dedicated to trip planning. You key in where you want to go, and it helps you arrange everything from hotel and dinner reservations to sightseeing.

In a recent article we discussed the topic of cybersecurity and gave you 10 publicly traded cyber security companies you could invest in to play this theme. As with any technology niche, some of the most exciting players are often startups because they are high risk and high reward. For retail investors, it becomes very difficult to invest in startups but nonetheless you should be aware of what they are up to because those publicly traded stocks you hold might just be displaced by a nimble startup. So how can we tell which cybersecurity startups are the hottest? The best way is to follow the money and look at what venture capital (VC) investors think is hot.

Salesforce is announcing today the launch of its Einstein artificial intelligence (A.I.) platform that's implemented into several of the company's existing cloud services: Sales Cloud, Service Cloud, Marketing Cloud, Analytics Cloud, App Cloud, Commerce Cloud, Community Cloud, and IoT Cloud. The company is also announcing the formation of Salesforce Research, a unit that will do research in deep learning, natural language processing, and computer vision that can be used to improve Salesforce products. The unit is led by Salesforce chief scientist Richard Socher, formerly cofounder and chief executive of A.I. startup MetaMind, which Salesforce acquired earlier this year. In a press briefing in San Francisco this week, Socher declined to say how many people were part of the team, although he did say that some of Salesforce's 175 data scientists have joined the newly organized division. The product enhancements will have an impact on Salesforce's direct competitors.