Skip to content

Joni Salminen Posts

How to teach machines common sense? Solutions for ambiguity problem in artificial intelligence

Introduction The ambiguity problem illustrated: User: “Siri, call me an ambulance!” Siri: “Okay, I will call you ‘an ambulance’.” You’ll never reach the hospital, and end up bleeding to death. Solutions Two potential solutions: A. machine builds general knowledge (“common sense”) B. machine identifies ambiguity & asks for clarification from humans The whole “common sense” problem can be solved by…

Hakukoneoptimointi toimittajan näkökulmasta

Johdanto Media on riippuvainen mainostuloista. On jatkuva kiistelyn aihe, miten paljon toimittajien tulisi kirjoittaa juttuja, jotka saavat klikkejä ja näyttöjä suhteessa juttuihin, joiden yhteiskunnallinen merkitys on korkea. Nämä kaksi kun eivät aina kulje käsi kädessä. Sosiaalisen median ja hakukoneiden merkitys toimittajan työssä Käytännössä toimittajat joutuvat työnsä puolesta huomioimaan juttujen kiinnostavuuden sosiaalisessa mediassa. Tämä on tärkeää vaikka haluaisi kirjoittaa vain yhteiskunnallisesti…

Affinity analysis in political social media marketing – the missing link

Introduction. Hm… I’ve figured out how to execute successful political marketing campaign on social media [1], but one link is missing still. Namely, applying affinity analysis (cf. market basket analysis). Discounting conversions. Now, you are supposed to measure “conversions” by some proxy – e.g., time spent on site, number of pages visited, email subscription. Determining which measurable action is the…

Total remarketing – the concept

Here’s a definition: Total remarketing is remarketing in all possible channels with all possible list combinations. Channels: Programmatic display networks (e.g., Adroll) Google (GDN, RLSA) Facebook (Website Custom Audience) Facebook (Video viewers / Engaged with ads) etc. How to apply: Test 2-3 different value propositions per group Prefer up-selling and cross-selling over discounts (the goal is to increase AOV, not…

Koneoppimisen jämähtämisongelma

Konekin voi joskus jäätyä. Kone oppii kuten ihminen: empiirisen havaintoaineiston (= datan) perusteella. Tästä syystä samoin kuin ihmisen on hankala oppia pois huonoista tavoista ja asenteista (ennakkoluulot, stereotypiat), on koneen vaikea oppia nopeasti pois virheellisestä tulkinnasta. Kysymys ei ole poisoppimisesta, mikä lienee monessa tapauksessa mahdotonta, vaan uuden oppimisesta, niin että vanhat muistirakenteet (= mallin ominaisuudet) korvataan tehokkaasti uusilla. Tehokkaasti, koska…

In 2016, Facebook bypassed Google in ads. Here’s why.

Introduction The gone 2016 was the first year I thought Facebook ends up beating Google in the ad race, despite the fact Google still dominates in revenue ($67Bn vs. $17Bn in 2015). I’ll explain why. First, consider that Google’s growth is restricted by three things: natural demand keyword volumes, and approach of perfect market. More demand than supply First, at…

Buying and selling complement bundles: When individual selling maximizes profit

Introduction When we were young, me and my brother used to buy and sell game consoles on Huuto.net (local eBay) and on various gamer discussion forums (Konsolifin BBS, for example). We didn’t have much money, so this was a great way to earn some cash — plus it taught us some useful business lessons along the years. What we would…

Polling social media users to predict election outcomes

The 45th President of the USA Introduction The problem of predicting election outcomes with social media is that the data, such as likes, are aggregate, whereas the election system is not — apart from simple majority voting, in which you only have the classic representativeness problem that Gallup solved in 1936. To solve the aggregation problem, one needs to segment…

Analyzing sentiment of topical dimensions in social media

Introduction Had an interesting chat with Sami Kuusela from Underhood.co. Based on that, got some inspiration for an analysis framework which I’ll briefly describe here. The model Figure 1 Identifying and analyzing topical text material The description User is interested in a given topic (e.g., Saara Aalto, or #saaraaalto). He enters the relevant keywords. The system runs a search and…