On November 15, 2017, TIBCO announced the acquisition of Alpine Data, a data science platform long known for its goals of democratizing data science and simplifying access to data, analytic workflows, parallel compute, and tools.
With this acquisition, TIBCO makes its second major foray into the machine learning space after June 5th acquisition of Statistica. In doing so, TIBCO has significantly upgraded its machine learning support capabilities, which will be especially useful to TIBCO in continuing to position itself as a full-range data and analytics solution.
When this acquisition occurred, Amalgam received questions on how Alpine Data and Statistica would be expected to work together and how Alpine Data would fit into TIBCO’s existing machine learning and analytics portfolio. Amalgam has provided favorable recommendations for both Alpine Data and Statistica in 2017 and plans to continue providing a positive recommendation for both solutions, but sought to explore the nuances of these recommendations.
In our Market Milestone, we explore why Alpine Data was a lower-ranked machine learning solution in analyst landscapes despite being early-to-market in providing strong collaborative capabilities and supporting a wide variety of data sources. We also wanted to explore the extent to which Alpine Data provided some sort of conflict to existing TIBCO customers. Finally, we also wanted to provide guidance on how TIBCO’s acquisition would potentially change Alpine Data’s positioning and capabilities.
To read Amalgam Insights’ view and recommendations regarding this report, use the following link to acquire this report.
myTrailhead allows customized training content and incorporates useful motivational and performance testing tools.
myTrialhead could be enhanced by incorporating scientifically-validated best practices in training, which suggest that hard skills are best trained by a cognitive skill learning system in the brain and soft skills are best trained by a behavioral skill learning system in the brain
In its current implementation, myTrailhead is more nearly optimized for hard skill training, but is sub-optimal for soft skills training
What do Bill O’Reilly, Harvey Weinstein, Roy Moore, Louis C.K., Michael Oreskes, Kevin Spacey and many others have in common (other than all being male)? Certainly not political beliefs or professional expertise. Whether left or right leaning in the political arena, or focused on entertainment, journalism or government service, all have been accused of, and in some cases admitted to, sexual harassment or sexual assault.
Sexual misconduct has been a cancer on society for as long as history has been recorded. Have we reached a tipping point? Has the “#MeToo” movement and the press coverage led to a fundamental shift in our thinking and will it permanently affect behavior? These are great questions, that I am not qualified to answer. Only time will tell.
What I am prepared to say is that our approach to sexual harassment awareness, in particular, training programs focused specifically on increasing awareness of sexual harassment and reducing the incidence of sexual harassment, are nearly all sub-optimal.
Computer-Based Sexual Harassment Awareness Training is Sub-Optimal
Whether developed for government or corporate entities large and small, nearly all sexual harassment awareness training programs are classroom or computer-based. They involve having individuals read text, or watch slideshows and videos that define sexual harassment and the behaviors that are appropriate or inappropriate. They describe power differentials that often exist in government or the corporate world and how that impacts the appropriateness of interpersonal interactions. They might even include video interactions so that individuals can “see” sexual harassment in action from a third-person perspective.
Appropriate interpersonal interactions and real-time communication skills are best learned by the behavioral skills learning system in the brain that learns by doing and receiving immediate corrective feedback. Physical repetitions, not mental repetitions, are key. Genuine empathy for another’s situation is best trained through a first-person experience in which you “are” that other person.
The Promise of VR for Sexual Harassment Awareness Training
VR offerings currently come in two general types. One takes a first-person perspective and allows you to literally “walk a mile” in someone else’s shoes. This approach involves passive, observational learning, much like computer based training, but the feeling of immersion, and more importantly the feeling that you are “someone else” is powerful. I believe that this offers one of the most effective tools for enhancing emotional intelligence and helping learners understand at a visceral level what it is like to be in a position of weakness and to be the direct target of sexual harassment. There is no better way for a middle-aged, Caucasian male to “feel” the prejudice or sexual harassment that a young, female African-American might experience or to “feel” the discrimination that many members of the LGBT community feel, than to put that man in a first-person VR environment where they are that other individual. Of course, the training content and the training scenarios must be realistic to be effective, but experts in this sector know how to create high-quality content. In my view, first-person VR experiences offer a great first step toward reducing the incidence of sexual harassment by increasing genuine empathy and understanding.
Although these passive, observational VR experiences offer a great tool for enhancing sexual harassment awareness, they are not focused specifically on behavior. The second type of VR offering, interactive VR, addresses this problem directly. Interactive VR platforms incorporate realistic interpersonal interaction and real-time communication into the mix. The learner can be placed in situations involving sexual harassment in which virtual agents react to the learner’s behavior in real-time. In other words, learners learn by doing and by receiving immediate feedback regarding the correctness of their behavior. This approach optimally recruits the behavioral skills learning system in the brain, which is the ideal system for reducing the incidence of inappropriate behaviors. Without taking a deep dive into brain neurochemistry, suffice it to say that behavioral skills learning is best when the brain circuits that initiated the behavior are still active when feedback is received. If the action is appropriate, then that behavior will be strengthened, and if the action is inappropriate, then that behavior will be weakened. Although there are clearly ethical limits to the intensity of the VR environments that one can be compelled to experience, interactive VR experiences with even mild levels of harassment will be effective in changing behavior.
Interactive VR approaches may also be useful in extreme cases as a rehabilitation procedure. Individuals already identified as sexual harassers by previous actions or complaints may benefit significantly from this type of rehabilitative behavioral therapy. In these situations, it may be ethically appropriate to increase the intensity of the interactive VR environments so that real changes in behavior will occur.
Sexual harassment is a serious problem in our society. In many cases, the individual is fully aware of their behavior and simply does not care. In such cases, no training, whether computer-based or VR, will likely have any effect. These are situations involving a conscious bias and behavioral change may be difficult. It is the cases of unconscious bias, where the individual is less aware of the impact of their behavior, that there is hope. The point of this article is not to claim that all sexual harassment can be eradicated. That is unrealistic, wishful thinking. That said, I believe that we can reduce the incidence of sexual harassment through effective training. I believe that the science of learning suggests that VR may provide a better tool for achieving this goal than computer based training.
I am not an expert on sexual harassment, but I do understand the psychology of behavior and behavior change. Although traditional computer-based approaches do their best to define, describe and demonstrate sexual harassment behavior, they target the cognitive skills learning system in the brain. This system is ideal for hard skill training, but not soft skill training, such as the training needed to reduce the incidence sexual harassment. I believe that VR holds significant promise as a training tool for reducing the incidence of sexual harassment. By combining the passive, observational first-person VR experiences that allow one to see the world through someone else’s eyes and experience sexual harassment first hand, with interactive VR experiences that allow one to engage in interpersonal interaction and real-time communication focused on rewarding appropriate behaviors and punishing inappropriate behaviors we might be able to reduce the size of this cancer from our society. The science is clear, and it suggests that this VR approach has merit.
Key Takeaway: Amalgam believes that the go-live date of myEinstein will be the most important date for Enterprise AI in 2018 as it represents the day that AI will become practical and available to a broad business audience across industries, verticals, company sizes, and geographies.
On November 6, 2017, Salesforce [NYSE:CRM] announced the launch of myEinstein: services based on Salesforce’s Einstein machine learning platform to support point-and-click-based and codeless AI app development. This announcement was one of several new services that Salesforce built across platform (mySalesforce and myIoT), training (myTrailhead), and user interface development (myLightning).
As the fall season of tech conferences starts to wind down, something is quite clear – machine learning (ML) is going to be everywhere. Box is putting ML in content management, Microsoft in office and CRM, and Oracle is infusing it into, well, everything. While not a great leap forward on the order of the Internet, smartphones, or PCs, the inclusion of ML technology into so many applications will make a lot of mundane tasks easier. This trend promises to be a boon for developers. The strength of machining learning is finding and exploiting patterns and anomalies. What could be more useful to developers?